MATEC Web Conf.
Volume 336, 20212020 2nd International Conference on Computer Science Communication and Network Security (CSCNS2020)
|Number of page(s)||8|
|Section||Artificial Recognition and Application|
|Published online||15 February 2021|
Research on driverless vehicle vision algorithm
Tianjin University of Technology and Education, Tianjin 300222, China
* Corresponding author: email@example.com
Obstacle detection in complex urban traffic environment has become an important part of unmanned vehicle optimization, and its complexity brings great challenges to the reliability of unmanned target detection. YOLOv3 in deep learning algorithm has a good detection effect in target detection, but it has certain defects in detecting targets in complex urban traffic environment. In this paper, the spatial pyramid module is added to YOLOv3 to improve the extraction of data features of the deep model. Then, on the basis of optimized network, the target detection algorithm is streamlined by combining layer pruning and channel pruning. The streamlined algorithm is called YOLOv3-SPP3-Tiny. Comparing the experimental results of YOLOv3-SPP3-tiny and YOLOv3 on Street Scenes dataset, the Precision is improved by 2.77%, the average precision (mAP) is increased by 0.87%, the Total BFLOPS is reduced by 94.49%, and the Inference time is reduced by 80.39%. Experimental results show that the model YOLOv3-SPP3-tiny algorithm is more conducive to unmanned object detection in complex urban road environment.
Key words: YOLOv3-SPP3-tiny / Complex scenes / Target detection / Model pruning
© The Authors, published by EDP Sciences, 2021
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