Research on Detection Method for Driving Scenarios Based on Multi-stage Parameter Fusion Network

doi:10.3901/JME.2024.10.064

Abstract

Abstract: It is difficult to meet the requirements of both accuracy and speed when applied to intelligent vehicle controllers for object detection based on deep learning methods. Therefore, a multi-stage parameter fusion object detection method for driving scenarios has been proposed, achieving an improvement for detection speed and accuracy simultaneously. Firstly, a multi-stage branching structure is designed to build the model, at the same time, to improve the speed of model inference, the multi-stage branching structure is equivalent to a single convolution-batch normalization layer by introducing a parameter fusion method, whose parameters are reduced greatly with unchanged generalization. Secondly, to improve detection accuracy, a bounding box loss function of SSIoU(Soft scaled intersection of union) and a united semi-anchor free labeling assignment are put forward, enhancing model adaptability to driving scenarios. Finally, the experiments are conducted on the DAIR-V2X-V dataset, the results show that the approach proposed achieves 9.89% and 51.89% improvements in mAP(mean average precision) and FPS(Frames per second) compared to the state-of-the-art YOLO (You only look once) algorithm.

Key words: intelligent vehicle, object detection, parameter fusion, SSIoU, YOLO algorithm

CLC Number:

TP183

LIN Chen, HE Zhicheng, HUANG Yifei, LIN Zhigui, FU Guang, HUANG Jin. Research on Detection Method for Driving Scenarios Based on Multi-stage Parameter Fusion Network[J]. Journal of Mechanical Engineering, 2024, 60(10): 64-75.

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