YOLOv7 模型於小物件檢測之改良與應用

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姓名

周彥廷(Yen-Ting Chou) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

YOLOv7 模型於小物件檢測之改良與應用
(Application of Improved YOLOv7 on Small Object Detection)

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至系統瀏覽論文 (2024-12-31以後開放)

摘要(中)

本研究目的為改良YOLOv7物件偵測模型於小物件偵測之能力。本研究總整前人於YOLOv4、YOLOv5等模型上的提升方法，包含調整模型輸出、更改骨幹結構、使用CBAM注意力機制模組、以K-means++聚類算法計算錨框以及使用無錨框檢測頭的改良方法。將上述方法與綜合應用提出各種改動後，本研究使用了回收玻璃的資料集訓練這些改動模型，並且進行結果的分析與討論。根據結果，本研究發現於小物件偵測時使用K-means++聚類算法來計算錨框之結果較差。最佳的組合是調整了backbone與輸出，同時加入了CBAM模組與使用了無錨框檢測頭的模型。相較於初始的YOLOv7模型，本研究提出的改良模型能成功將測試資料的mAP數值提升8.7%。本研究對小物件偵測的數種改善方法實際測試並提出相應理由，並成功的提升YOLOv7於小物件的偵測能力。

摘要(英)

The purpose of this study is to improve the capability of the object detection model YOLOv7 in detecting small objects. The study integrates previous enhancement methods used in models of YOLOv4 and YOLOv5, including adjusting the model output, modifying the backbone structure, incorporating the CBAM attention mechanism module, using the K-means++ clustering algorithm to calculate anchor boxes, and employing the Anchor-Free Detection Head for anchor-less detection. By applying and combining these methods, the study trained the modified models using a dataset of recycled glass and conducted an analysis and discussion of the results. Based on the findings, the study observed that using the K-means++ clustering algorithm for anchor box calculation yielded inferior results in small object detection. The optimal combination involved adjusting the backbone and output, incorporating the CBAM module, and utilizing the anchor-free detection head. Compared to the original YOLOv7 model, the modified model in this study successfully increased the mAP value by 8.7%. The study practically tested and provided corresponding justifications for several improvement methods in small object detection, effectively enhancing the detection YOLOv7 capability for small objects.

關鍵字(中)

★ YOLOv7
★ Small Object Detection
★ K-means++
★ CBAM
★ Anchor Free Detection Head

關鍵字(英)

論文目次

摘要 i
亮點 i
ABSTRACT ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章緒論 1
1-1 背景與動機 1
1-2 文獻回顧 2
1-3 研究目的 4
第二章相關技術介紹 5
2-1 深度學習影像偵測指標分類與指標 5
2-2 YOLO模型的發展歷程 10
2-3 YOLO v7模型的架構 12
2-4 現有的玻璃色選機 16
2-5 提升小物件辨識的方法 19
第三章研究方法 25
3-1 本研究預計使用的模型更動 26
3-2 各項改動的組合 30
3-3 數值目標與目標速度 33
第四章實驗設計 35
4-1 資料集建立 35
4-2 模型訓練環境 38
4-3 模型訓練參數 39
第五章結果與討論 43
5-1 單項模型改動結果比較 43
5-2 多項模型改動結果比較 46
5-3 模型訓練小結 51
5-4 實際於回收玻璃的應用 52
第六章結論與建議 53
6-1 具體貢獻 53
6-2 應用 53
6-3 限制 53
6-4 未來展望 54
參考文獻 55
附錄 58

參考文獻

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指導教授

林錦德(Chin-Te Lin)

審核日期

2023-7-27

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