Optimization Algorithm of Autonomous Target Recognition for Unmanned Vehicles Based on YOLOv5

doi:10.12382/bgxb.2022.1161

Abstract

Abstract:

Autonomous target recognition is a key technology for enabling intelligent operation of unmanned vehicles in complex ground battlefield environments. However, deploying and applyingdeep learning-based autonomous target recognition algorithmson resource-constrained embedded computing platformsfor realizing intelligent unmanned vehicles remains a challenging task. Based on the YOLOv5 deep neural network structure used for autonomous recognition of ground targets, this paper proposes a multi regularization adaptive network pruning algorithm based on animproved attention module and BatchNorm layer. The proposed algorithm achieves optimal pruning of the network structure during the collaborative process of pruning and training. A combined posttraining INT8 quantization algorithm is designed, employing unsaturated mapping for weights and saturated mapping for activation values. The compressed and optimized YOLOv5x network is then deployed and verified on the embedded computing platform based on the Zynq UltraScale+ MPSoC architecture.The verification results show that when YOLOv5x network prunes 40% of the channel and quantizes with INT8 strategy, the recognition accuracy for infrared dataset is only reduced by 0.374%. The recognition accuracy for visible light dataset is increased by 0.065%, and the target recognition frame rate can reach 25 frames per second. The optimized network can meet the accuracy and real-time requirements of autonomous target recognition in the complex battlefield environment of unmanned vehicles. The proposed network optimization algorithm can be extended to other combat platforms, such as unmanned aerial vehiclesand precision-guided weapons.

Key words: unmanned vehicles, target recognition, YOLOv5x, regularized pruning, INT8 quantization algorithm, embedded platform

CLC Number:

TP391.4

ZHAO Xiaodong, ZHANG Xunying. Optimization Algorithm of Autonomous Target Recognition for Unmanned Vehicles Based on YOLOv5[J]. Acta Armamentarii, 2023, 44(9): 2732-2744.

Figures/Tables 22

References 26

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算法	像素	参数量/10⁶	FLOPs/10⁹
YOLOv5s	416×416	7.2	6.9
YOLOv5m	416×416	21.2	20.7
YOLOv5l	416×416	46.5	46.1
YOLOv5x	416×416	86.7	86.8

算法	像素	参数量/10⁶	FLOPs/10⁹
YOLOv5s	416×416	7.2	6.9
YOLOv5m	416×416	21.2	20.7
YOLOv5l	416×416	46.5	46.1
YOLOv5x	416×416	86.7	86.8

CSP编号	网络类型
CSP编号	YOLOv5s	YOLOv5m	YOLOv5l	YOLOv5x
CSP1_1	1	2	3	4
CSP1_2	3	6	9	12
CSP1_3	3	6	9	12
CSP2_1	1	2	3	4
CSP2_2	1	2	3	4
CSP2_3	1	2	3	4
CSP2_4	1	2	3	4
CSP2_5	1	2	3	4

CSP编号	网络类型
CSP编号	YOLOv5s	YOLOv5m	YOLOv5l	YOLOv5x
CSP1_1	1	2	3	4
CSP1_2	3	6	9	12
CSP1_3	3	6	9	12
CSP2_1	1	2	3	4
CSP2_2	1	2	3	4
CSP2_3	1	2	3	4
CSP2_4	1	2	3	4
CSP2_5	1	2	3	4

数据集	裁剪比率/%	精度mAP(变化幅度/%)
		红外未裁剪0.9882		可见光未裁剪0.9215
		文献[19]算法	ReLU+Sigmoid		Mish+Sigmoid
红外数据集	20	0.9897 (+0.152)	0.9903 (+0.213)		0.9915 (+0.334)
	30	0.9848 (-0.344)	0.9855 (-0.273)		0.9861 (-0.213)
	40	0.9842 (-0.405)	0.9851 (-0.314)		0.9858 (-0.243)
	可见光数据集	20	0.9244 (+0.315)	0.9251 (+0.39)		0.9258 (+0.467)
30		0.9238 (+0.25)	0.9243 (+0.304)		0.9253 (+0.412)
40		0.9218 (+0.033)	0.9225 (+0.109)		0.9229 (+0.152)