An improved GhostNet for unsafe driving behavior algorithm
Shuyin Tang, Huasheng Zhu, Yang Yang, Zhanxin Sun, Yongjian Li · 2023
The existing unsafe driving behavior detection algorithms are difficult to meet the requirements of good real-time performance and high precision. This paper proposes an improved GhostNet unsafe driving behavior detection algorithm. The algorithm uses the Ghost convolution operator to reduce the complexity of the algorithm and improve the real-time performance. In addition, a dual-scale time adaptive module (DCTAM) is designed to extract the temporal information. Through fusing spatial and temporal information to improve the accuracy of detecting unsafe driving behaviors. The experimental results show that the algorithm in this paper reduces the computational complexity and stabilizes the detection accuracy.