Small-Target Detection Network Based on Adaptive Feature Enhancement

Mengmeng Wu; Zebin Zhang; Yaozhe Song; Ziting Shu; Baoqing Li

doi:10.3788/LOP213048

Laser & Optoelectronics Progress, Volume. 60, Issue 6, 0610004(2023)

Small-Target Detection Network Based on Adaptive Feature Enhancement

Mengmeng Wu^1,2, Zebin Zhang¹, Yaozhe Song^1,2, Ziting Shu^1,2, and Baoqing Li^1、*

¹Key Laboratory of Microsystem Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 201800, China

²University of Chinese Academy of Sciences, Beijing 100049, China

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Figures & Tables(10)

Fig. 1. Details of YOLOv5l network

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Fig. 2. Structure of YOLO-AFENet

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Fig. 3. Structures of PANet and M-BiFPN. (a) PANet; (b) M-BiFPN

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Fig. 4. Structure of SAGN module

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Fig. 5. Input image and ROI label

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Fig. 6. AP curves of YOLO-AFENet

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Fig. 7. Comparison of detection results of YOLO-AFENet and YOLOv5 on test dataset. (a) YOLOv5; (b) YOLO-AFENet

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Table 1. Size Distribution of object in dataset
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Table 1. Size Distribution of object in dataset
Object
Small object
（size≤32×32）
Medium object
（32×32≤size≤96×96）
Large object
（size≥96×96）
Total
Number 494117（61.9%） 290826（36.4%） 13852（1.7%） 798795（100%）

Table 2. Comparison of results of different object detection algorithms on UAVDT dataset

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Table 2. Comparison of results of different object detection algorithms on UAVDT dataset

Detector	Input	backbone	AP /%	AP₅₀ /%	AP₇₅ /%	AP_S /%	AP_M /%	AP_L /%	FPS
R-FCN^［23］	600×1000	ResNet-101	7.0	17.5	3.9	4.4	14.7	12.1	9
SSD	512×512	VGG16	9.3	21.4	6.7	7.1	17.1	12.0	22
RON^［24］			5.0	15.9	1.7	2.9	12.7	11.2
FRCNN	600×1000	VGG16	5.8	17.4	2.5	3.8	12.3	9.4	7
FRCNN+FPN		ResNet50	11.0	23.4	8.4	8.1	20.2	26.5	18
ClusDet^［25］	600×1000	ResNet50	13.7	26.5	12.5	9.1	25.1	31.2
DMNet^［26］	600×1000	ResNet50	14.7	24.6	16.3	9.3	26.2	35.2
YOLOv5	640×640	CSPDarkNet53	16.7	29.3	16.7	10.2	25.5	33.0	55.65
YOLO-AFENet	640×640	CSPDarkNet53	19.4	35.6	17.6	11.9	28.6	35.3	52.86

Table 3. Ablation experiment on UAVDT dataset
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Table 3. Ablation experiment on UAVDT dataset
Baseline YOLOv5 With M-BiFPN With SAGN AP /% AP₅₀ /% AP₇₅ /% AP_S /% AP_M /% AP_L /% FPS
√ 16.7 29.3 16.7 10.2 25.5 33.0 55.65
√ √ 17.5 34.47 17.0 10.6 27.7 34.5 54.21
√ √ √ 19.4 35.6 17.6 11.9 28.6 35.3 52.86

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Mengmeng Wu, Zebin Zhang, Yaozhe Song, Ziting Shu, Baoqing Li. Small-Target Detection Network Based on Adaptive Feature Enhancement[J]. Laser & Optoelectronics Progress, 2023, 60(6): 0610004

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