用於邊緣計算的全新輕量化物件偵測系統

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姓名

張育珉(Yu-Min Zhang) 查詢紙本館藏

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資訊工程學系

論文名稱

用於邊緣計算的全新輕量化物件偵測系統
(CSL-YOLO: A New Lightweight Object Detection System for Edge Computing)

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摘要(中)

由於高階的GPU始終定價較高體積較大的等一些較高的門檻，開發輕量級的物件偵測器至關重要，為了減少計算資源的無謂損耗，如何降低冗餘的計算起著重要的作用。本論文提出了一種全新的輕量級卷積方法Cross-Stage Lightweight(CSL) Module，它以廉價的運算來生成較為冗餘的特徵圖。在中間擴展深度的階段，我們將過去使用的pointwise convolution更換為depthwise convolution以生成候選的特徵圖。我們提出的CSL-Module可以顯著地降低計算成本，在CIFAR-10上進行的實驗表明了CSL-Module可以逼近convolution-3x3的擬合能力。最後我們以CSL-Module及其衍伸模組為基礎建構了全新的輕量級物件偵測器CSL-YOLO，與Tiny-YOLOv4相比，在MS-COCO上進行的實驗表明了CSL-YOLO僅以其43% FLOPs和52% parameters即可達到更好的物件偵測性能，達到了state-of-the-art的水準。

摘要(英)

The development of lightweight object detectors is essential due to the limited computation resources. To reduce the computation cost, how to generate redundant features plays a significant role. This paper proposes a new lightweight Convolution method Cross-Stage Lightweight (CSL) Module, to generate redundant features from cheap operations. In the intermediate expansion stage, we replaced Pointwise Convolution with Depthwise Convolution to produce candidate features. The proposed CSL-Module can reduce the computation cost significantly. Experiments conducted at MS-COCO show that the proposed CSL-Module can approximate the fitting ability of Convolution-3x3. Finally, we use the module to construct a lightweight detector CSL-YOLO, achieving better detection performance with only 43% FLOPs and 52% parameters than Tiny-YOLOv4.

關鍵字(中)

★ 輕量級物件偵測器

關鍵字(英)

★ YOLO
★ MS-COCO

論文目次

1. 前言 1
2. 文獻回顧 3
2.1. 輕量級卷積方法 3
2.2. 輕量級物件偵測器 6
2.2.1. SSD系列 6
2.2.2. YOLO系列 6
2.2.3. SSD與YOLO的主要差異 7
3. 研究方法 8
3.1. CSL-Module 8
3.1.1. 比較其他輕量級卷積方法 9
3.1.2. 理論速度分析 10
3.2. 構建輕量級元件 11
3.2.1. 輕量級骨幹網路CSL-Bone 12
3.2.2. 輕量級特徵金字塔特徵網路CSL-FPN 13
4. 實作細節及局部實驗 14
4.1. CSL-Module的實作細節 14
4.2. CSL-Bone的實作細節 15
4.3. CSL-FPN的實作細節 17
4.4. CSL-YOLO的實作細節 18
4.4.1. Anchors Constraint 18
4.4.2. Non-Exponential Prediction 19
4.4.3. 損失函數 20
5. 實驗結果 22
5.1. 資料集 22
5.2. 在MS-COCO上測試CSL-YOLO 23
6. 結論 26
6.1. 貢獻 26
6.2. 未來展望 27
7. 參考文獻 28

參考文獻

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指導教授

范國清謝君偉(Kuo-Chin Fan Jun-Wei Hsieh)

審核日期

2021-8-2

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