Fast vehicle detection algorithm in traffic scene based on improved SSD. (30th September 2022) - Chen, Zhichao; Guo, Haoqi; Yang, Jie; Jiao, Haining; Feng, Zhicheng; Chen, Lifang; Gao, Tao
Abstract: For autonomous driving systems, vehicle detection is an important part and challenging problem due to the complex traffic scenes and poor computing resources. This paper proposes an improved SSD(single shot mutlibox detector) algorithm for the fast detection of vehicles in traffic scenes. MobileNet v2 is selected as the backbone feature extraction network for SSD, which improves the real-time performance of the algorithm. To improve detection accuracy, the channel attention mechanism is utilized for feature weighting, and the deconvolution module is utilized to construct a bottom–top feature fusion structure. The experimental results show that the average precision of the proposed algorithm on the BDD100K and KITTI datasets is 82.59% and 84.83%, respectively. The single inference time of the algorithm is 73ms, which is only about 5/11 of the original model, realizing the improvement of inference speed and prediction accuracy. Highlights: Vision-based surrounding vehicle detection is utilized for intelligent driving environment sensing to ensure driving safety. The proposed method has three improvements to the single shot mutlibox detector (SSD), including a lightweight backbone network, the attention mechanism, and the deconvolution module. The proposed method simplifies hardware requirements, reduces computational costs, and meets the needs of practical application. The proposed method could automatically detect vehicles in various complex traffic scenes.