摘要
近年來人工智慧領域的發展相當迅速且全面，尤其是以神經網路架構為主的深度學習領域最為熱門。其中深度學習物件辨識領域的辨識準確度逐年上升，已經能夠應用於現實生活。但是針對小物件方面的準確度卻仍舊低下。至2020年中為止，在公開數據集挑戰賽，對於小物件辨識最佳方法其準確度仍舊難以突破50%。因此本研究的目的在於提高深度學習的物件辨識領域中，對於小物件辨識的準確度。本研究對象為鳥類偵測，利用鳥類偵測應用於驅趕野鳥。
本研究分別基於小物件偵測數據增強法、課程學習、困難反例探勘，這三種數據增強方法改進和融合，並用於改善模型辨識小物件的準確度。於實驗中，驗證不同方法對相同模型的辨識準確度之改進效果。本研究以鳥類偵測為例驗證對於真實環境下此方法依舊有效，並結合開發的硬體設備，於飼養場域中驅趕野鳥，以此預防家禽感染禽流感。
本研究共進行兩項主要實驗。實驗一：改進小物件偵測數據增強法於模型準確度與實際場域之應用分析，以及實驗二：改進課程學習結合困難反例探勘於模型準確度與實際場域之應用分析。針對改善模型辨識小物件準確度的實驗結果顯示，本研究提出改善小物件辨識的兩種方法對於原始模型於小物件辨識準確度都有大幅度的進步。原始模型於小物件數據集中的準確度為18%，而實驗一中達到24%的偵測效果，而實驗二於實驗一之上又提升偵測效果達到30.48%；此外實驗一中，使用本研究所開發的實驗設備結合改進的數據增強方法於真實場域的應用效果較差，僅有13.22%的驅逐效果，而實驗二所改進的數據增強方法結合實驗設備於真實場域的應用效果大幅領先於實驗一，驅逐效果達到27.70%。

Abstract
In recent years, the field of artificial intelligence is growing quickly and comprehensively. Particularly, deep learning, based on neural network architecture, is the most popular technology. In object recognition field of deep learning, the recognition accuracy is increasing year by year which is appliable to real life. However, the recognition accuracy for small object is still relatively low. Until June 2020, the recognition accuraucy for small object, in the open datasets challenge, was unlikely to be more than 50%. Therefore, the purpose of this study was to improve the recognition accuracy of small objects in this field. The objective of this study was to apply in bird detection, which is used to drive away wild birds.
This study utilized the algorithms of augmentation for small object detection, curriculum learning and hard negative mining, that are three famous data augmentation methods. Those methods were improved, fused, and used again to improve the recognition accuracy for small objects. The improvement of the recognition accuracy of the same model by different methods was verified in experiments. Then, these methods were applied to real-life situations to verify that the method was still effective, such as bird detection, and in integration with the hardware equipment to drive away birds and to prevent the poultry of being infected with avian influenza.
Two experiments were conducted in this study. 1) Experiment 1: improving augmentation for small object detection in model accuracy and real-field application analysis, and 2) Experiment 2: improving and fusing curriculum learning and hard negative mining for model accuracy and real-world application analysis. The results of experiments showed that two proposed methods in this study had significant improvements in the recognition accuracy for small objects. The recognition accuracy without improvement was 18%. By constract, our detection accuracy in experiment 1 was as good as 24%, and in experiment 2 was up to 30.48%. In addition, by integration of the experimental laser equipment based on Experiment 1, the effect rate of driving bird away was less than 13.22%. Based on the same laser equipment, the effect rate in Experiment 2 was significantly improved to be 27.70%.

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