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研究生: 鍾宜修
Chung, Yi-Hsiu
論文名稱: 針對空拍影像物件偵測之改良型YOLOv7演算法研究
An Improved YOLOv7 Algorithm for Object Detection on UAV Images
指導教授: 蘇崇彥
Su, Chung-Yen
口試委員: 蘇崇彥
Su, Chung-Yen
彭昭暐
Perng, Jau-Woei
賴以威
Lai, I-Wei
口試日期: 2024/06/12
學位類別: 碩士
Master
系所名稱: 電機工程學系
Department of Electrical Engineering
論文出版年: 2024
畢業學年度: 112
語文別: 中文
論文頁數: 48
中文關鍵詞: 深度學習物件偵測YOLOv7無人機小物件
英文關鍵詞: Deep learning, Object detection, YOLOv7, UAV, Small object
DOI URL: http://doi.org/10.6345/NTNU202400899
論文種類: 學術論文
相關次數: 點閱:359下載:8
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近幾年無人機的技術發展迅速,飛行距離越來越遠、體積也不斷縮小,甚至能自動飛行,因此能應用的範圍也越來越廣泛,例如交通監測、工業或自然環境巡檢等等。另外隨著人工智慧的興起,現在無人機也會結合人工智慧演算法協助其辨識影像。由於無人機所拍攝的影像內物件往往尺寸偏小,且無人機本身的運算支援有限,因此如何提升小物件的辨識效果且同時降低模型運算時所需的資源至關重要。
本論文以YOLOv7為基礎模型進行改良,提升它對小物件的偵測效果且同時降低模型參數量及計算量,我們以VisDrone-DET2019資料集來驗證模型改良成效。總共修改五種方式,第一種方式是將ELAN (Efficient Layer Aggregation Network)替換成M-ELAN (Modified Efficient Layer Aggregation Network),第二種方式是在高階特徵層添加M-FLAM (Modified Feature Layer Attention Module),第三種方式是將特徵融合的結構從PANet (Path Aggregation Network)改成ResFF (Residual Feature Fusion),第四種方式是將模型內下採樣的模塊改成I-MP模塊 (Improved MaxPool Module),最後一種方式是將SPPCSPC (Spatial Pyramid Pooling Cross Stage Partial Networks)替換成GSPP(Group Spatial Pyramid Pooling)。綜合以上方法,將mAP (mean Average Precision)提升1%,同時模型參數量卻下降24.5%,模型計算量GFLOPs (Giga Floating Point of Operations)也降低13.7%。

In recent years, the advancement of unmanned aerial vehicle technology has been rapid, with increased flying distances, reduced sizes, and even autonomous capabilities. Consequently, the scope of applications has expanded significantly, including traffic surveillance, industrial inspections, and environmental monitoring. Moreover, with the rise of artificial intelligence, UAV now integrate AI algorithms to aid in image recognition. However, due to the typically small size of objects captured by drones and limited computational resource, enhancing the recognition of small objects while simultaneously reducing the computational required is crucial.
This paper proposes improvements to the YOLOv7 as base model to enhance its ability to detection small objects while reducing model parameters and computation volume. We validate the model enhancements using the VisDrone-DET2019 dataset. There are five modifications. First of all, replacing Efficient Layer Aggregation Network (ELAN) with Modified Efficient Layer Aggregation Network (M-ELAN). Secondly, adding Modified Feature Layer Attention Module (M-FLAM) at high-level feature layers. Then modifying the feature fusion structure from Path Aggregation Network (PANet) to Residual Feature Fusion (ResFF). The fourth modification is replacing the downsampling modules within the model with I-MP (Improved MaxPool) modules. Finally, replacing Spatial Pyramid Pooling Cross Stage Partial Networks (SPPCSPC) with Group Spatial Pyramid Pooling (GSPP).
Combining these methods led to a 1% increase in mean Average Precision (mAP), while reducing model parameters by 24.5% and decreasing computation volume Giga Floating Point of Operations (GFLOPs) by 13.7%.

誌 謝 i 中文摘要 ii Abstract iii 目 錄 v 表 目 錄 vii 圖 目 錄 viii 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 2 1.3 論文架構 2 第二章 文獻探討 3 2.1 物件偵測演算法 3 2.1.1 兩階段物件偵測演算法 4 2.1.2 一階段物件偵測演算法 6 2.2 YOLOv7模型 9 2.2.1 輸入層 (Input) 10 2.2.2 Backbone 11 2.2.3 Neck 12 2.2.4 Prediction 14 2.2.5 NMS (Non-Maximum Suppression) 15 2.2.6 mAP (mean Average Precision) 16 第三章 基於YOLOv7提升空拍影像之物件偵測效果之改良方法 19 3.1 M-ELAN (Modified Efficient Layer Aggregation Network) 19 3.2 M-FLAM (Modified Feature Layer Attention Module) 20 3.3 ResFF (Residual Feature Fusion) 22 3.4 I-MP (Improved MaxPool Module) 24 3.5 GSPP (Group Spatial Pyramid Pooling) 27 3.6 改良的YOLOv7架構 29 第四章 實驗結果與討論 30 4.1 硬體及環境配置 30 4.2 實驗設置 31 4.3 VisDrone-DET2019資料集介紹 32 4.4 實驗結果與分析 33 4.5 其他資料集實驗結果 39 4.5.1 PCB Defect資料集 39 4.5.2 PASCAL Visual Object Classes 2007資料集 41 第五章 結論與未來展望 42 參考文獻 43 自 傳 47 學術成就 48

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