人工智慧的進步以更精密的方式迅速改變了交通系統。智慧交通系統結合行人、道路及車輛相關資訊，以提供更高的安全性、效率和舒適性。智慧交通系統中的每個元素相互牽制並都應被保留及作後續的改善。電腦視覺的最新進展在許多方面豐富了機器感知的能力。
因此，本研究旨在提出一種基於智能視覺的方法，以加強智慧交通系統在協助、維護和監控方面的效果。在輔助方面，本研究著重於駕駛睡意偵測系統和交通場景分割，提出基於深度學習的即時駕駛睡意偵測系統，試圖提高極端場景下的睡意檢測品質。在交通場景分割中，利用了邊緣和特徵級別過濾方式，以達到更好的切割效果。
此外，本論文提出基於知識轉移的方法來生成一個強大且有效的模型。在維護方面，本論文提出經改良的道路裂紋檢測方法，著重於裂紋細化和具自動資源映射的高效檢測器。最後，監控方面基於視訊濃縮，能夠生成短、密集且緊湊的視頻。整體而言，上述六項成果在智慧交通系統的進步方面皆具有巨大的潛力，與文獻裏現有方法相比，本論文所提出的方法在各項指標上皆有突出的表現。

The advancement of artificial intelligence rapidly transforms the transportation system in a more sophisticated manner. The intelligent transportation
system (ITS) is an integrated system comprised of people, roads, and vehicles. Each element inside the ITS is demanding reciprocally which should
be preserved and require improvements. Recent advancements in computer
vision enrich the capability of machine perception in many aspects. Hence,
this study aims to propose an intelligent vision-based method to enhance
the ITS in assistance, maintenance, and surveillance aspects. In the assistance aspect, the study focuses on driver drowsiness systems and traffic
scene segmentation. A real-time driver drowsiness system based on deep
learning is proposed, which attempts to improve the quality of drowsiness
detection in extreme scenarios. In traffic scene segmentation, the edges
and feature level filtering are utilized. In addition, a knowledge transfer-based approach is proposed to generate a robust and efficient model. In the
maintenance aspect, an improved crack detection is proposed, which focus on the crack refinement and efficient detector with automatic resource
mapping. Finally, the surveillance aspect is based on video synopsis, and
is able to generate short, yet dense and compact video. Overall, the aforementioned six efforts have a huge potential to improve the advancement of
the intelligent transportation system, and they are examined to provide the
state-of-the-art performance compared with the existing methods.

[1] J.-M. Guo and H. Markoni, “Driver drowsiness detection using hybrid convolutional neural network and long short-term memory,” Multimedia tools and applications, vol. 78, no. 20, pp. 29059–29087, 2019.
[2] J.-M. Guo and H. Markoni, “Image semantic segmentation with edge and feature level attenuators,” in 2019 IEEE International Conference on Image Processing (ICIP), pp. 2511–2515, 2019.
[3] J.-M. Guo, H. Markoni, and J.-D. Lee, “Barnet: Boundary aware refinement network for crack detection,” IEEE Transactions on Intelligent Transportation Systems, pp. 1–16, 2021.
[4] A. Sahayadhas, K. Sundaraj, and M. Murugappan, “Detecting driver drowsiness based on sensors: a review,” Sensors, vol. 12, no. 12, pp. 16937–16953, 2012.
[5] R. N. Khushaba, S. Kodagoda, S. Lal, and G. Dissanayake, “Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 1, pp. 121–131, 2011.
[6] D. Wu, V. J. Lawhern, S. Gordon, B. J. Lance, and C.-T. Lin, “Driver drowsiness estimation from eegsignals using online weighted adaptation regularization for regression (owarr),” IEEE Transactions on Fuzzy Systems, vol. 25, no. 6, pp. 1522–1535, 2016.
[7] W. Zhang, B. Cheng, and Y. Lin, “Driver drowsiness recognition based on computer vision technology,” Tsinghua Science and Technology, vol. 17, no. 3, pp. 354–362, 2012.
[8] S. Park, F. Pan, S. Kang, and C. D. Yoo, “Driver drowsiness detection system based on feature representation learning using various deep networks,” in Asian Conference on Computer Vision, pp. 154–164, Springer, 2016.
[9] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems, vol. 25, pp.1097–1105, 2012.
[10] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
[11] S. Ji, W. Xu, M. Yang, and K. Yu, “3d convolutional neural networks for human action recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 221–231, 2013.
[12] A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014.
[13] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.
[14] M. Akopyan and E. Khashba, “Large-scale youtube-8m video understanding with deep neural networks,” CoRR, vol. abs/1706.04488, 2017.
[15] C.-Y. Ma, M.-H. Chen, Z. Kira, and G. AlRegib, “Ts-lstm and temporal-inception: Exploiting spatiotemporal dynamics for activity recognition,” Signal Processing: Image Communication, vol. 71, pp. 76–87, 2019.
[16] T.-H. Shih and C.-T. Hsu, “Mstn: Multistage spatial-temporal network for driver drowsiness detection,” in Asian Conference on Computer Vision, pp. 146–153, Springer, 2016.
[17] J. Yu, S. Park, S. Lee, and M. Jeon, “Representation learning, scene understanding, and feature fusion for drowsiness detection,” in Asian Conference on Computer Vision, pp.165–177, Springer, 2016.
[18] P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, pp. I–I, 2001.
[19] S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International conference on machine learning, pp.448–456, PMLR, 2015.
[20] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The journal of machine learning research, vol. 15, no. 1, pp. 1929–1958, 2014.
[21] M. T. McCann, K. H. Jin, and M. Unser, “A review of convolutional neural networks for inverse problems in imaging,” arXiv preprint arXiv:1710.04011, 2017.
[22] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
[23] R. C. Gonzalez, R. E. Woods, et al., “Digital image processing second edition,” Beijing: Publishing House of Electronics Industry, vol. 455, 2002.
[24] K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, “Joint face detection and alignment using multitask cascaded convolutional networks,” IEEE Signal Processing Letters, vol. 23, no. 10, pp. 1499–1503, 2016.
[25] K. He and J. Sun, “Convolutional neural networks at constrained time cost,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015.
[26] M. Lin, Q. Chen, and S. Yan, “Network in network,” arXiv preprint arXiv:1312.4400, 2013.
[27] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015.
[28] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
[29] Y. Miao, J. Li, Y. Wang, S.-X. Zhang, and Y. Gong, “Simplifying long short-term memory acoustic models for fast training and decoding,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2284–2288, 2016.
[30] V. Campos, B. Jou, X. Giró-i-Nieto, J. Torres, and S. Chang, “Skip RNN: learning to skip state updates in recurrent neural networks,” CoRR, vol. abs/1708.06834, 2017.
[31] C.-H. Weng, Y.-H. Lai, and S.-H. Lai, “Driver drowsiness detection via a hierarchical temporal deep belief network,” in Asian Conference on Computer Vision, pp. 117–133, Springer, 2016.
[32] S. Xie, R. Girshick, P. Dollar, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017.
[33] G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017.
[34] R. Girshick, “Fast r-cnn,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), December 2015.
[35] S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Advances in neural information processing systems, vol. 28, pp. 91–99, 2015.
[36] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in European conference on computer vision, pp. 21–37, Springer, 2016.
[37] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
[38] K. He, G. Gkioxari, P. Dollar, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), Oct 2017.
[39] A. Chaurasia and E. Culurciello, “Linknet: Exploiting encoder representations for efficient semantic segmentation,” in 2017 IEEE Visual Communications and Image Processing (VCIP), pp. 1–4, 2017.
[40] O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, pp. 234–241, Springer, 2015.
[41] F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571, 2016.
[42] A. Paszke, A. Chaurasia, S. Kim, and E. Culurciello, “Enet: A deep neural network architecture for real-time semantic segmentation,” arXiv preprint arXiv:1606.02147, 2016.
[43] S. Mehta, M. Rastegari, A. Caspi, L. Shapiro, and H. Hajishirzi, “Espnet: Efficient spatial pyramid of dilated convolutions for semantic segmentation,” in Proceedings of the European Conference on Computer Vision (ECCV), September 2018.
[44] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Semantic image segmentation with deep convolutional nets and fully connected crfs,” arXiv preprint arXiv:1412.7062, 2014.
[45] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 4, pp. 834–848, 2018.
[46] L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam, “Rethinking atrous convolution for semantic image segmentation,” arXiv preprint arXiv:1706.05587, 2017.
[47] L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proceedings of the European Conference on Computer Vision (ECCV), September 2018.
[48] M. Drozdzal, E. Vorontsov, G. Chartrand, S. Kadoury, and C. Pal, “The importance of skip connections in biomedical image segmentation,” in Deep learning and data labeling for medical applications, pp. 179–187, Springer, 2016.
[49] I. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio, “Maxout networks,” in International conference on machine learning, pp. 1319–1327, PMLR, 2013.
[50] S. Estrada, S. Conjeti, M. Ahmad, N. Navab, and M. Reuter, “Competition vs. concatenation in skip connections of fully convolutional networks,” in International Workshop on Machine Learning in Medical Imaging, pp. 214–222, Springer, 2018.
[51] E. Romera, J. M. Álvarez, L. M. Bergasa, and R. Arroyo, “Erfnet: Efficient residual factorized convnet for real-time semantic segmentation,” IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 1, pp. 263–272, 2018.
[52] C.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu, “Deeply-supervised nets,” in Artificial intelligence and statistics, pp. 562–570, PMLR, 2015.
[53] G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” arXiv preprint arXiv:1503.02531, 2015.
[54] D. Guo, Y. Pei, K. Zheng, H. Yu, Y. Lu, and S. Wang, “Degraded image semantic segmentation with dense-gram networks,” IEEE Transactions on Image Processing, vol. 29, pp. 782–795, 2020.
[55] V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 12, pp. 2481–2495, 2017.
[56] P. Chao, C.-Y. Kao, Y.-S. Ruan, C.-H. Huang, and Y.-L. Lin, “Hardnet: A low memory traffic network,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019.
[57] M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
[58] G. J. Brostow, J. Fauqueur, and R. Cipolla, “Semantic object classes in video: A high-definition ground truth database,” Pattern Recognition Letters, vol. 30, no. 2, pp. 88–97, 2009.
[59] C. Yu, J. Wang, C. Peng, C. Gao, G. Yu, and N. Sang, “Bisenet: Bilateral segmentation network for real-time semantic segmentation,” in Proceedings of the European Conference on Computer Vision (ECCV), September 2018.
[60] H. Zhao, X. Qi, X. Shen, J. Shi, and J. Jia, “Icnet for real-time semantic segmentation on highresolution images,” in Proceedings of the European Conference on Computer Vision (ECCV), September 2018.
[61] H. Li, P. Xiong, H. Fan, and J. Sun, “Dfanet: Deep feature aggregation for real-time semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
[62] S. Mehta, M. Rastegari, L. Shapiro, and H. Hajishirzi, “Espnetv2: A light-weight, power efficient, and general purpose convolutional neural network,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
[63] T. Wu, S. Tang, R. Zhang, J. Cao, and Y. Zhang, “Cgnet: A light-weight context guided network for semantic segmentation,” IEEE Transactions on Image Processing, vol. 30, pp. 1169–1179, 2021.
[64] C. Kamann and C. Rother, “Increasing the robustness of semantic segmentation models with paintingby-numbers,” in European Conference on Computer Vision, pp. 369–387, Springer, 2020.
[65] R. Kapela, P. Śniatała, A. Turkot, A. Rybarczyk, A. Pożarycki, P. Rydzewski, M. Wyczałek, and A. Błoch, “Asphalt surfaced pavement cracks detection based on histograms of oriented gradients,” in 2015 22nd International Conference Mixed Design of Integrated Circuits Systems (MIXDES), pp. 579–584, 2015.
[66] L. Meng, Z. Wang, Y. Fujikawa, and S. Oyanagi, “Detecting cracks on a concrete surface using histogram of oriented gradients,” in 2015 International Conference on Advanced Mechatronic Systems (ICAMechS), pp. 103–107, 2015.
[67] F. Liu, G. Xu, Y. Yang, X. Niu, and Y. Pan, “Novel approach to pavement cracking automatic detection based on segment extending,” in 2008 International Symposium on Knowledge Acquisition and Modeling, pp. 610–614, 2008.
[68] Y. Quan, J. Sun, Y. Zhang, and H. Zhang, “The method of the road surface crack detection by the improved otsu threshold,” in 2019 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 1615–1620, 2019.
[69] B. Chen, X. Zhang, R. Wang, Z. Li, and W. Deng, “Detect concrete cracks based on otsu algorithm with differential image,” ipi, vol. 1, p. 0, 2019.
[70] A. Chatterjee and Y.-C. Tsai, “A fast and accurate automated pavement crack detection algorithm,” in 2018 26th European Signal Processing Conference (EUSIPCO), pp. 2140–2144, 2018.
[71] M. Quintana, J. Torres, and J. M. Menéndez, “A simplified computer vision system for road surface inspection and maintenance,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 3, pp. 608–619, 2016.
[72] X. Liu, F. Xue, and L. Teng, “Surface defect detection based on gradient lbp,” in 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), pp. 133–137, 2018.
[73] R. Medina, J. Llamas, E. Zalama, and J. Gómez-García-Bermejo, “Enhanced automatic detection of road surface cracks by combining 2d/3d image processing techniques,” in 2014 IEEE International Conference on Image Processing (ICIP), pp. 778–782, 2014.
[74] S. Chanda, G. Bu, H. Guan, J. Jo, U. Pal, Y.-C. Loo, and M. Blumenstein, “Automatic bridge crack detection–a texture analysis-based approach,” in IAPR Workshop on Artificial Neural Networks in Pattern Recognition, pp. 193–203, Springer, 2014.
[75] M. Avila, S. Begot, F. Duculty, and T. S. Nguyen, “2d image based road pavement crack detection by calculating minimal paths and dynamic programming,” in 2014 IEEE International Conference on Image Processing (ICIP), pp. 783–787, 2014.
[76] S. Agarwal and D. Singh, “An adaptive statistical approach for non-destructive underline crack detection of ceramic tiles using millimeter wave imaging radar for industrial application,” IEEE Sensors Journal, vol. 15, no. 12, pp. 7036–7044, 2015.
[77] C. Premachandra, H. Waruna, H. Premachandra, and C. D. Parape, “Image based automatic road surface crack detection for achieving smooth driving on deformed roads,” in 2013 IEEE International Conference on Systems, Man, and Cybernetics, pp. 4018–4023, 2013.
[78] C. Sun and B. Appleton, “Multiple paths extraction in images using a constrained expanded trellis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 12, pp. 1923–1933, 2005.
[79] V. Baltazart, P. Nicolle, and L. Yang, “Ongoing tests and improvements of the mps algorithm for the automatic crack detection within grey level pavement images,” in 2017 25th European Signal Processing Conference (EUSIPCO), pp. 2016–2020, 2017.
[80] P. Subirats, J. Dumoulin, V. Legeay, and D. Barba, “Automation of pavement surface crack detection using the continuous wavelet transform,” in 2006 International Conference on Image Processing, pp. 3037–3040, 2006.
[81] Y. Zuo, G. Wang, and C. Zuo, “Wavelet packet denoising for pavement surface cracks detection,” in 2008 International Conference on Computational Intelligence and Security, vol. 2, pp. 481–484, 2008.
[82] S. Wu and Y. Liu, “A segment algorithm for crack dection,” in 2012 IEEE Symposium on Electrical Electronics Engineering (EEESYM), pp. 674–677, 2012.
[83] H. Oliveira and P. L. Correia, “Road surface crack detection: Improved segmentation with pixelbased refinement,” in 2017 25th European Signal Processing Conference (EUSIPCO), pp. 2026–2030, 2017.
[84] P. J. Dawson, E. De Sena, and P. A. Naylor, “An acoustic image-source characterisation of surface profiles,” in 2018 26th European Signal Processing Conference (EUSIPCO), pp. 2130–2134, 2018.
[85] Q. Zou, Y. Cao, Q. Li, Q. Mao, and S. Wang, “Cracktree: Automatic crack detection from pavement images,” Pattern Recognition Letters, vol. 33, no. 3, pp. 227–238, 2012.
[86] R. Amhaz, S. Chambon, J. Idier, and V. Baltazart, “Automatic crack detection on two-dimensional pavement images: An algorithm based on minimal path selection,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 10, pp. 2718–2729, 2016.
[87] H. Li, D. Song, Y. Liu, and B. Li, “Automatic pavement crack detection by multi-scale image fusion,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 6, pp. 2025–2036, 2019.
[88] S. Liang, X. Jianchun, and Z. Xun, “An algorithm for concrete crack extraction and identification based on machine vision,” IEEE Access, vol. 6, pp. 28993–29002, 2018.
[89] Y. Pan, X. Zhang, G. Cervone, and L. Yang, “Detection of asphalt pavement potholes and cracks based on the unmanned aerial vehicle multispectral imagery,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 10, pp. 3701–3712, 2018.
[90] P. Sheng, L. Chen, and J. Tian, “Learning-based road crack detection using gradient boost decision tree,” in 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1228–1232, 2018.
[91] Y. Shi, L. Cui, Z. Qi, F. Meng, and Z. Chen, “Automatic road crack detection using random structured forests,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 12, pp. 3434–3445, 2016.
[92] H. Oliveira and P. L. Correia, “Automatic road crack detection and characterization,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 1, pp. 155–168, 2013.
[93] V. Baltazart, L. Yang, P. Nicolle, and J.-M. Moliard, “Pseudo-ground truth data collection on pavement images,” in 2017 25th European Signal Processing Conference EUSIPCO), pp. 2021–2025, 2017.
[94] Y.-C. J. Tsai, A. Chatterjee, and C. Jiang, “Challenges and lessons from the successful implementation of automated road condition surveys on a large highway system,” in 2017 25th European Signal Processing Conference (EUSIPCO), pp. 2031–2035, 2017.
[95] D. A. Chacra and J. Zelek, “Municipal infrastructure anomaly and defect detection,” in 2018 26th European Signal Processing Conference (EUSIPCO), pp. 2125–2129, 2018.
[96] Z. Qu, L. Bai, S.-Q. An, F.-R. Ju, and L. Liu, “Lining seam elimination algorithm and surface crack detection in concrete tunnel lining,” Journal of Electronic Imaging, vol. 25, no. 6, p. 063004, 2016.
[97] D. Fernandes, P. L. Correia, and H. Oliveira, “Road surface crack detection using a light field camera,” in 2018 26th European Signal Processing Conference (EUSIPCO), pp.2135–2139, 2018.
[98] Q. Li, D. Zhang, Q. Zou, and H. Lin, “3d laser imaging and sparse points grouping for pavement crack detection,” in 2017 25th European Signal Processing Conference (EUSIPCO), pp. 2036–2040, 2017.
[99] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014.
[100] X. Qin, Z. Zhang, C. Huang, C. Gao, M. Dehghan, and M. Jagersand, “Basnet: Boundary-aware salient object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
[101] M. Feng, H. Lu, and E. Ding, “Attentive feedback network for boundary-aware salient object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
[102] Z. Wu, L. Su, and Q. Huang, “Cascaded partial decoder for fast and accurate salient object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
[103] W. Wang, S. Zhao, J. Shen, S. C. H. Hoi, and A. Borji, “Salient object detection with pyramid attention and salient edges,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
[104] S. Xie and Z. Tu, “Holistically-nested edge detection,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), December 2015.
[105] Y. Liu, M.-M. Cheng, X. Hu, K. Wang, and X. Bai, “Richer convolutional features for edge detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017.
[106] M. Eisenbach, R. Stricker, D. Seichter, K. Amende, K. Debes, M. Sesselmann, D. Ebersbach, U. Stoeckert, and H.-M. Gross, “How to get pavement distress detection ready for deep learning? a systematic approach,” in 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2039–2047, 2017.
[107] F. Yang, L. Zhang, S. Yu, D. Prokhorov, X. Mei, and H. Ling, “Feature pyramid and hierarchical boosting network for pavement crack detection,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 4, pp. 1525–1535, 2019.
[108] Z. Fan, C. Li, Y. Chen, P. D. Mascio, X. Chen, G. Zhu, and G. Loprencipe, “Ensemble of deep convolutional neural networks for automatic pavement crack detection and measurement,” Coatings, vol. 10, no. 2, p. 152, 2020.
[109] H. Tsuchiya, S. Fukui, Y. Iwahori, Y. Hayashi, W. Achariyaviriya, and B. Kijsirikul, “A method of data augmentation for classifying road damage considering influence on classification accuracy,” Procedia Computer Science, vol. 159, pp. 1449–1458, 2019.
[110] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015.
[111] Q. Zou, Z. Zhang, Q. Li, X. Qi, Q. Wang, and S. Wang, “Deepcrack: Learning hierarchical convolutional features for crack detection,” IEEE Transactions on Image Processing, vol. 28, no. 3, pp. 1498–1512, 2018.
[112] H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena, “Self-attention generative adversarial networks,” in International conference on machine learning, pp. 7354–7363, PMLR, 2019.
[113] S. Wu, J. Fang, X. Zheng, and X. Li, “Sample and structure-guided network for road crack detection,” IEEE Access, vol. 7, pp. 130032–130043, 2019.
[114] W. Song, G. Jia, D. Jia, and H. Zhu, “Automatic pavement crack detection and classification using multiscale feature attention network,” IEEE Access, vol. 7, pp. 171001–171012, 2019.
[115] Z. Fan, C. Li, Y. Chen, J. Wei, G. Loprencipe, X. Chen, and P. Di Mascio, “Automatic crack detection on road pavements using encoder-decoder architecture,” Materials, vol. 13, no. 13, p. 2960, 2020.
[116] Y. Liu, J. Yao, X. Lu, R. Xie, and L. Li, “Deepcrack: A deep hierarchical feature learning architecture for crack segmentation,” Neurocomputing, vol. 338, pp. 139–153, 2019.
[117] T.-Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017.
[118] K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio, “Show, attend and tell: Neural image caption generation with visual attention,” in International conference on machine learning, pp. 2048–2057, PMLR, 2015.
[119] J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.
[120] S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, “Cbam: Convolutional block attention module,” in Proceedings of the European Conference on Computer Vision (ECCV), September 2018.
[121] D. Sun, A. Yao, A. Zhou, and H. Zhao, “Deeply-supervised knowledge synergy,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
[122] Z. Shen, Z. Liu, J. Li, Y.-G. Jiang, Y. Chen, and X. Xue, “Object detection from scratch with deep supervision,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 2, pp. 398–412, 2020.
[123] C. Li, M. Z. Zia, Q.-H. Tran, X. Yu, G. D. Hager, and M. Chandraker, “Deep supervision with intermediate concepts,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 8, pp. 1828–1843, 2019.
[124] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems, pp. 5998–6008, 2017.
[125] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020.
[126] S. Wang, B. Z. Li, M. Khabsa, H. Fang, and H. Ma, “Linformer: Self-attention with linear complexity,” arXiv preprint arXiv:2006.04768, 2020.
[127] V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing atari with deep reinforcement learning,” arXiv preprint arXiv:1312.5602, 2013.
[128] R. S. Sutton, D. A. McAllester, S. P. Singh, Y. Mansour, et al., “Policy gradient methods for reinforcement learning with function approximation.,” in NIPs, vol. 99, pp. 1057–1063, Citeseer, 1999.
[129] V. R. Konda and J. N. Tsitsiklis, “Actor-critic algorithms,” in Advances in neural information processing systems, pp. 1008–1014, 2000.
[130] V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, “Asynchronous methods for deep reinforcement learning,” in International conference on machine learning, pp. 1928–1937, PMLR, 2016.
[131] Z. Wang, E. Simoncelli, and A. Bovik, “Multiscale structural similarity for image quality assessment,” in The Thrity-Seventh Asilomar Conference on Signals, Systems Computers, 2003, vol. 2, pp. 1398–1402 Vol.2, 2003.
[132] A. Rav-Acha, Y. Pritch, and S. Peleg, “Making a long video short: Dynamic video synopsis,” in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 1, pp. 435–441, 2006.
[133] Y. Pritch, A. Rav-Acha, and S. Peleg, “Nonchronological video synopsis and indexing,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 11, pp. 1971–1984, 2008.
[134] Y. Pritch, A. Rav-Acha, A. Gutman, and S. Peleg, “Webcam synopsis: Peeking around the world,” in 2007 IEEE 11th International Conference on Computer Vision, pp. 1–8, 2007.
[135] Y. Pritch, S. Ratovitch, A. Hendel, and S. Peleg, “Clustered synopsis of surveillance video,” in 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 195–200, 2009.
[136] C.-R. Huang, H.-C. Chen, and P.-C. Chung, “Online surveillance video synopsis,” in 2012 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1843–1846, 2012.
[137] C.-R. Huang, P.-C. J. Chung, D.-K. Yang, H.-C. Chen, and G.-J. Huang, “Maximum <italic>a posteriori</italic> probability estimation for online surveillance video synopsis,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 24, no. 8, pp. 1417–1429, 2014.
[138] X. Zhu, J. Liu, J. Wang, and H. Lu, “Key observation selection for effective video synopsis,” in Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp. 2528–2531, 2012.
[139] S. Feng, Z. Lei, D. Yi, and S. Z. Li, “Online content-aware video condensation,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2082–2087, 2012.
[140] W. Fu, J. Wang, L. Gui, H. Lu, and S. Ma, “Online video synopsis of structured motion,” Neurocomputing, vol. 135, pp. 155–162, 2014.
[141] J. Zhu, S. Feng, D. Yi, S. Liao, Z. Lei, and S. Z. Li, “High-performance video condensation system,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 7, pp. 1113–1124, 2015.
[142] Y. He, Z. Qu, C. Gao, and N. Sang, “Fast online video synopsis based on potential collision graph,” IEEE Signal Processing Letters, vol. 24, no. 1, pp. 22–26, 2017.
[143] Y. Nie, C. Xiao, H. Sun, and P. Li, “Compact video synopsis via global spatiotemporal optimization,” IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 10, pp. 1664–1676, 2013.
[144] L. Sun, J. Xing, H. Ai, and S. Lao, “A tracking based fast online complete video synopsis approach,” in Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp. 1956–1959, 2012.
[145] A. Yildiz, A. Ozgur, and Y. S. Akgul, “Fast non-linear video synopsis,” in 2008 23rd International Symposium on Computer and Information Sciences, pp. 1–6, 2008.
[146] S. Wang, J. Yang, Y. Zhao, A. Cai, and S. Z. Li, “A surveillance video analysis and storage scheme for scalable synopsis browsing,” in 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1947–1954, 2011.
[147] S. Feng, S. Liao, Z. Yuan, and S. Z. Li, “Online principal background selection for video synopsis,” in 2010 20th International Conference on Pattern Recognition, pp. 17–20, 2010.
[148] S. Chen, X. Liu, Y. Huang, C. Zhou, and H. Miao, “Video synopsis based on attention mechanism and local transparent processing,” IEEE Access, vol. 8, pp. 92603–92614, 2020.
[149] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, “Focal loss for dense object detection,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), Oct 2017.
[150] X. Li, Z. Wang, and X. Lu, “Video synopsis in complex situations,” IEEE Transactions on Image Processing, vol. 27, no. 8, pp. 3798–3812, 2018.
[151] T. Ruan, S. Wei, J. Li, and Y. Zhao, “Rearranging online tubes for streaming video synopsis: A dynamic graph coloring approach,” IEEE Transactions on Image Processing, vol. 28, no. 8, pp. 3873–3884, 2019.
[152] K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Real-time foreground–background segmentation using codebook model,” Real-time imaging, vol. 11, no. 3, pp. 172–185, 2005.
[153] J.-M. Guo, Y.-F. Liu, C.-H. Hsia, M.-H. Shih, and C.-S. Hsu, “Hierarchical method for foreground detection using codebook model,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, no. 6, pp. 804–815, 2011.
[154] J.-M. Guo, C.-H. Hsia, Y.-F. Liu, M.-H. Shih, C.-H. Chang, and J.-Y. Wu, “Fast background subtraction based on a multilayer codebook model for moving object detection,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 10, pp. 1809–1821, 2013.
[155] O. Barnich and M. Van Droogenbroeck, “Vibe: A universal background subtraction algorithm for video sequences,” IEEE Transactions on Image Processing, vol. 20, no. 6, pp. 1709–1724, 2011.