Lightweight UAV image target detection algorithm based on YOLOv8

doi:10.11996/JG.j.2095-302X.2024061328

Abstract

Abstract:

To address the problems of low target pixels, complex backgrounds, and difficult model deployment in unmanned aerial vehicle (UAV) images, a lightweight multi-scale feature fusion small target detection algorithm based on YOLOv8 was proposed. In order to reduce the number of network parameters and improve the model detection speed, the FasterNet block was used to replace the bottleneck of C2f, resulting in the construction of a lightweight feature extraction module, FasterC2f. To enhance the multi-scale feature fusion ability of the model, a new focus diffusion feature was designed that enables each layer feature map of the neck network to focus on three layers of feature information. A shared convolution detection head was designed, allowing each detection head to contain feature information from different scales while optimizing the model parameters. The small target detection network was reconstructed to utilize a larger-scale three-layer detection head, improving the model’s feature learning capability for small targets. Experimental results on the Visdrone data set indicated that compared with YOLOv8s, the precision rate, recall rate, and mAP of this model increased by 5.1%, 5.4%, and 6.6%, respectively. The number of parameters was reduced by 68%, and the model file size decreased by 15.3 MB, while FPS increased by 16%. These results demonstrated that the model possesses advantages in high detection accuracy, fast detection speed, and ease of deployment.

Key words: YOLOv8, UAV, small target detection, lightweight, feature fusion

CLC Number:

YAN Jianhong, RAN Tongxiao. Lightweight UAV image target detection algorithm based on YOLOv8[J]. Journal of Graphics, 2024, 45(6): 1328-1337.

Figures/Tables 16

References 29

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参数名称	参数值
epoch	300
batch-size	8
初始学习率	0.01
优化器	SGD
momentum	0.937
图像分辨率	640×640

参数名称	参数值
epoch	300
batch-size	8
初始学习率	0.01
优化器	SGD
momentum	0.937
图像分辨率	640×640

实验	P/ %	R/ %	mAP/ %	Weight/ MB	FPS
YOLOv8s	50.2	38.0	38.8	22.5	110
替换backbone	49.9	38.6	39.4	18.7	238
替换neck	50.3	37.9	39.3	18.8	161
全部替换	49.0	37.6	38.8	16.0	251

实验	P/ %	R/ %	mAP/ %	Weight/ MB	FPS
YOLOv8s	50.2	38.0	38.8	22.5	110
替换backbone	49.9	38.6	39.4	18.7	238
替换neck	50.3	37.9	39.3	18.8	161
全部替换	49.0	37.6	38.8	16.0	251

模型	P/ %	R/ %	mAP/ %	Weight/ MB	FPS
YOLOv8s	50.2	38.0	38.8	22.5	110
YOLOv8s+BiFPN	50.0	39.9	40.7	56.7	149
YOLOv8s+AFPN	49.6	36.1	37.7	17.2	207
YOLOv8s+FD-FPN	51.5	38.2	39.9	21.1	176