A visual detection algorithm for autonomous driving road environment perception.

Achieving accurate and real-time perception of environmental targets in complex traffic scenes based on visual sensors is a challenging research problem in the field of autonomous driving technology. In methods to date, it is difficult to effectively balance the detection accuracy and speed. To this end, this paper proposes an interactive and lightweight visual detection algorithm – YRDM (Your Region Decision-Making) – based on the concepts of efficient mining and utilisation of target feature information, lightweight network structure, and optimisation of label allocation for highly practical detection of ambient targets in autonomous driving scenarios. First, a two-stage algorithm architecture consisting of four low-parameter subnetworks is constructed with the goal of efficiently mining and utilising target feature information, and the accuracy and effectiveness of the algorithm are balanced through the interaction of information between the subnetworks. Second, in order to further improve the detection speed, lightweight convolution is introduced into the structure of the YRDM network to construct the DSC3 module, which allows lightweight processing of the subnetwork structure. Finally, by converting the label assignment problem into an optimal transport problem, adaptation to the global nature of the samples by YRDM is improved, allowing better detection accuracy. The algorithm is tested with two major public datasets, BDD100K and KITTI, and a large number of experimental results show that the comprehensive performance of YRDM is better than other existing algorithms. In addition, ablation experiments and mobile terminal device deployment experiments further demonstrate the effectiveness and real-time performance of this algorithm. • Proposed a two-stage detection algorithm called your region decision-making (YRDM). • YRDM addressed challenges of complex traffic and limited computing resources. • YRDM achieved 57.8 % accuracy (BDD100K); 89.6 % (KITTI) with 2.98 GFLOPs. • Info interaction in low-parameter subnets boosts accuracy. • Lightweight convolution and DSC3 enhanced speed and efficiency. [ABSTRACT FROM AUTHOR]