Research on road target detection algorithm for autonomous driving based on improved YOLOv8

Li Lou; Ziyao Feng

doi:10.1117/12.3031588

18 July 2024 Research on road target detection algorithm for autonomous driving based on improved YOLOv8

Li Lou, Ziyao Feng

Proceedings Volume 13179, International Conference on Optics and Machine Vision (ICOMV 2024); 131790U (2024) https://doi.org/10.1117/12.3031588
Event: International Conference on Optics and Machine Vision (ICOMV 2024), 2024, Nanchang, China

Abstract

In order to solve the problems of missing detection and poor detection effect of small targets in autonomous driving scenarios, a road target detection algorithm with improved YOLOv8 algorithm was proposed. Firstly, the backbone network is replaced by FasterNet, which combines the multi-scale attention mechanism and depth separable convolution to improve the feature expression and receptive field range. Secondly, CBAM is integrated into the attention mechanism module, which combines the channel attention mechanism with the spatial attention mechanism to form a new convolutional block structure, so as to better carry out feature fusion. Finally, to solve the problem that CIOU loss function does not take into account the mismatch between the desired real frame and the predicted frame, Inner-SIoU loss function is introduced to effectively improve the accuracy of reasoning. Experimental results show that for the public Udacity data set, the proposed algorithm can improve the detection accuracy by2.9% while maintaining the same detection speed as the original algorithm.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Li Lou and Ziyao Feng "Research on road target detection algorithm for autonomous driving based on improved YOLOv8", Proc. SPIE 13179, International Conference on Optics and Machine Vision (ICOMV 2024), 131790U (18 July 2024); https://doi.org/10.1117/12.3031588

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KEYWORDS

Object detection

Detection and tracking algorithms

Roads

RGB color model

Autonomous driving

Autonomous vehicles

Convolution

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