基于改进YOLOv5s的红外舰船检测算法

doi:10.12305/j.issn.1001-506X.2023.08.15

Abstract

Abstract:

Infrared imaging seeker of anti-ship missile varies sharply in ship scale angle and its detection ability is poor when detecting small and weak ship targets. To solve this problem, a target detection algorithm based on improved YOLOv5s is proposed in this paper. Firstly, the depth separable convolution module is used to reduce the network model parameters. Secondly, the coordinate attention mechanism is introduced into the backbone network to improve the ability to pay attention to the target channel information features. Then, the adaptive spatial feature fusion strategy is used to optimize the spatial weight allocation. Finally, the loss function is improved to improve the reliability of the target detection frame. Comparative experiments verify that the proposed method improve the detection precision from the original 86.47% to 91.64%, and the mean average precision (mAP) index is improved from the original 85.56% to 89.35%. The improved algorithm also outperforms other target detection network models under the same conditions.

Key words: ship detection, infrared imagery, YOLOv5s, coordinate attention mechanism, feature fusion

CLC Number:

TP391.4

Haijun LI, Fancheng KONG, Yun LIN. Infrared ship detection algorithm based on improved YOLOv5s[J]. Systems Engineering and Electronics, 2023, 45(8): 2415-2422.

Figures/Tables 14

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Table 1

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References 30

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参数	取值
学习速度	0.01
优化器	Adam
基本大小	8
轮次	200

序号	DSC	CA	ASFF	参数量/10⁶	mAP/%	FPS
1	-	-	-	7.3	85.56	43.3
2	√	-	-	6.1	85.12	48.1
3	-	√	-	7.7	87.78	40.5
4	-	-	√	7.5	88.61	41.7
5	√	√	-	6.5	87.43	45.0
6	√	-	√	6.3	87.92	45.2
7	-	√	√	7.8	89.79	39.6
8	√	√	√	6.7	89.35	44.6

算法	P/%	R/%	mAP/%	FPS
Faster R-CNN	87.32	86.37	86.73	15.2
SSD	81.75	77.46	76.98	31.1
YOLOX	87.39	82.33	82.21	36.5
YOLOv4	83.93	79.67	79.06	36.7
Centernet	85.66	85.30	84.62	43.7
YOLOv5s	86.47	86.28	85.56	43.3
本文算法	91.64	89.71	89.35	44.6

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Infrared ship detection algorithm based on improved YOLOv5s

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