An improved GhostNet for unsafe driving behavior algorithm

Shuyin Tang; Huasheng Zhu; Yang Yang; Zhanxin Sun; Yongjian Li

doi:10.1117/12.2680117

27 June 2023 An improved GhostNet for unsafe driving behavior algorithm

Shuyin Tang, Huasheng Zhu, Yang Yang, Zhanxin Sun, Yongjian Li

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127053G (2023) https://doi.org/10.1117/12.2680117
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

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.

Citation Download Citation

Shuyin Tang, Huasheng Zhu, Yang Yang, Zhanxin Sun, and Yongjian Li "An improved GhostNet for unsafe driving behavior algorithm", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127053G (27 June 2023); https://doi.org/10.1117/12.2680117

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KEYWORDS

Convolution

Detection and tracking algorithms

Object detection

Video

Education and training

Image segmentation

Video acceleration

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