Multi-task panoramic driving perception algorithm based on improved YOLOv5
DOI:
https://doi.org/10.54097/hset.v34i.5489Keywords:
Deep learning, YOLOv5 algorithm, traffic object detection, driveable area segmentation, lane line detection.Abstract
Panoramic driving perception algorithm is becoming more and more important in the field of autonomous driving. A multi-task panoramic driving perception algorithm can quickly help vehicles make reasonable decisions. Aiming at the real-time high-precision problem of a multi-task panoramic driving perception algorithm, a multi-task panoramic Driving perception algorithm based on YOLOv5 structure is proposed. The algorithm consists of a backbone network for feature extraction and two branch networks for specific tasks. The C3 module in the original backbone network of YOLOv5 is replaced with an inversion residual bottleneck module to reduce the computation amount of the network and improve the network recognition accuracy. This algorithm proposes a new branch network, which can simultaneously train the segmentation of driveable area and lane detection, and improve the speed of feature extraction. In BDD100K, the public data set of Berkeley AI Laboratory, the detection speed of the algorithm proposed in this paper reaches 110FPS, which is 19FPS higher than that of the algorithm before improvement. The detection accuracy of traffic target detection reaches 77.2%, and the detection accuracy of lane line detection reaches 71.1%. It can well meet the real-time requirements of panoramic driving perception.
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