基于可变尺度先验框的声呐图像目标检测

doi:10.12305/j.issn.1001-506X.2024.03.01

Abstract

Abstract:

In recent years, target detection in sonar images using deep learning has become a hot research topic. However, sonar images have problems such as the concentration of target scale distribution and the difficulty of data acquisition, which makes the detection effect difficult to meet the requirements. A target detection method based on variable scale prior frame is proposed to address this issue. Firstly, considering the particularity of target scale distribution in sonar images, variable scale prior frames are generated based on prior statistics. Secondly, in order to solve the problem of sonar image scarcity, data augmentation methods are used to expand the training set. Finally, the lightweighting of the model is explored by deleting the large object detection layer of the model, simplifying the model structure without reducing model accuracy. In order to evaluate the effectiveness of the algorithm, comprehensive experiments are conducted on forward-looking sonar images as an example to determine the mean average precision (mAP)@0.75 and mAP@0.5:0.95 reached 0.585 and 0.559 respectively, which increased by 5.8% and 3.1% compared to the original Yolov5 network, while giga floating-point operations (GFLOPs) decreased to 14.9. The results show that the proposed algorithm has higher accuracy and a light weight model structure.

Key words: sonar image, target detection, data enhancement, scale clustering, lightweight model

CLC Number:

TP391.4

Sijia HUANG, Chunfeng SONG, Xuan LI. Target detection in sonar images based on variable scale prior frame[J]. Systems Engineering and Electronics, 2024, 46(3): 771-778.

Figures/Tables 8

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References 32

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特征图大小	先验框大小
80×80×256	[12, 25]	[19, 20]	[26, 21]
40×40×512	[20, 30]	[31, 26]	[41, 25]
20×20×1 024	[26, 41]	[39, 37]	[69, 33]

网络模型	简化网络结构	设置先验框	数据增强	mAP@0.5	mAP@0.75	mAP@0.5:0.95	GFLOPs
Yolov5s	-	-	-	0.962	0.527	0.528	16.5
Yolov5s	-	√	-	0.971	0.550	0.542	16.5
Yolov5s	-	√	√	0.970	0.555	0.547	16.5
Yolov5m	-	-	-	0.968	0.559	0.546	49.0
Yolov5l	-	-	-	0.964	0.546	0.542	109.1
Yolov5s	√	-	-	0.968	0.547	0.540	14.9
Yolov5s	√	√	-	0.966	0.579	0.552	14.9
Yolov5s	√	√	√	0.971	0.585	0.559	14.9

网络模型	简化网络结构	设置先验框	mAP@0.5	mAP@0.75	mAP@0.5:0.95	GFLOPs
Yolov5s	-	-	0.975	0.612	0.564	16.50
Faster R-CNN	-	-	0.535	0.242	0.202	63.29
文献[32]	-	-	0.974	0.621	0.569	16.50
Yolov5s	√	-	0.976	0.604	0.561	14.90
Yolov5s	-	√	0.973	0.630	0.569	16.50
Yolov5s	√	√	0.979	0.628	0.568	14.90

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Target detection in sonar images based on variable scale prior frame

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