我們使用 YOLOv4 作為主要模型開發用於紡織工業品質控制和非微小細胞肺癌檢測中即時目標檢之創新智慧系統。
紡織工業品質檢測系統以相對較快的檢測速度並以高精度檢測功能性紡織布料中的缺陷。該系統由品質檢測硬件和圖像處理軟件組成。我們開發的軟件使用數據預處理技術將原始圖像分解成合適的小尺寸。
利用影像濾波來去除雜訊和增強隱藏的特徵。為了創建系統穩健性使用數據擴充技術，然後分別標記圖像中缺陷的座標和分類。最後，我們利用 YOLOv4使用預訓練模型的權重進行訓練。
我們設計的軟件與設備硬件同時進行系統檢測。所設計的系統在缺陷檢測方面表現出色，準確率為 95.3%，召回率和 F1 得分分別為 93.6% 和 94.4%。在缺陷檢測方面表現出高性能，該系統實現了每秒 34 幀，推理時間為 21.4 毫秒。該系統能夠以高置信度即時檢測自功能性紡織面料缺陷。
在醫學成像方面，我們的目標是開發一個全自動系統，該系統將使用 YOLOv4 和基於區域的目標輪廓模型檢測、分割和準確地將非微小細胞肺癌腫瘤重建到空間中。該系統由兩個主要部分組成，即檢測和體積可視化。檢測部分由圖像增強、數據增強、標記和定位組成，而體積渲染主要是圖像濾波、腫瘤提取、基於區域的目標輪廓和三維重建。在該系統中，圖像被增強之前消除噪聲，數據擴充和多樣化圖像數據。然後進行標記，以便為分類模型建造強大的學習基礎。具有局部腫瘤的圖像通過平滑濾波器，然後收集並提取腫瘤遮罩。最後獲得輪廓信息以可視化重建腫瘤。所設計的系統顯示出強大的檢測性能，所設計的系統檢測速度為每秒 34 幀，每幅圖像的推理時間為 21.4 毫秒，精度為 96.6%，靈敏度和 F1 分數分別為 97% 和 96.8%。系統分割驗證在腫瘤提取方面實現了 92.2% 的 dice 得分係數。在使用三維重建腫瘤的 3D 打印圖像驗證該方法的體積可視化，獲得了 99.7% 的準確率。該系統顯示出強大的性能和可靠性，因為它能夠以高置信度檢測、分割和三維重建腫瘤。

We develop innovative intelligent systems for real-time object detection in textile industry quality inspection and non-small cell lung cancer tumor detection using YOLOv4 as a backbone model.
The textile industry quality inspection system aims at detecting defects in functional textile fabrics with high precision at a relatively fast detection speed. An image processing and a quality inspection hardware makes up the system. Through the development of the software, data preprocessing techniques were used to break down raw images into smaller suitable sizes. Image denoising and enhancing some hidden features were achieved through filtering. Data augmentation techniques were utilized to generalize and multiply the data to create robustness, followed by labeling where the defects were marked and tagged with their respective labels. Lastly, we employed YOLOv4, trained it with pretrained weights for localization. To implement the detection system, we deployed the software in the quality inspection hardware we designed. With a precision of 95.3%, recall and F1 score of 93.6% and 94.4% respectively, the designed system demonstrates a high performance in defect detection. The system achieved a relatively fast detection speed of 34 frames per second with an inference time of 21.4ms per image. As a result of this system, functional textile fabric defects can be detected in real-time with high confidence.
On the medical imaging we aim to develop a fully automatic system that will detect, segment and accurately reconstruct non-small cell lung cancer tumors into space using YOLOv4 and a region-based active contour model. The system consists of two main sections which are the detection and volumetric rendering. The detection section is composed of image enhancement, data augmentation, labeling and localization while the volumetric reconstruction is mainly image filtering, tumor extraction, contour extraction using region-based active contour and 3D rendering. In this system, the images are enhanced to eliminate noise before augmentation which is intended to multiply and diversify the image data. Labeling is then carried out in order to create a solid learning foundation for the localization model. Images with localized tumors were passed through smoothing filters and then clustered to extract tumor masks. Lastly contour information was obtained to render the volumetric tumor. The designed system displays a strong detection performance with a precision of 96.6%, sensitivity and F1 score of 97% and 96.8% respectively at a detection speed of 34 frames per second, inference time per image of 21.4ms. The system segmentation validation achieved a dice score coefficient of 92.2% on tumor extraction. A 99.7% accuracy was obtained during the verification of the method’s volumetric rendering using 3D printed images of the rendered tumors. This system shows a strong performance and reliability due to its ability to detect, segment and reconstruct a volumetric tumor into space with high confidence.

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