Research on Lightweight-Based Algorithm for Detecting Distracted Driving Behaviour

Abstract

In order to solve the existing distracted driving behaviour detection algorithms’ problems such as low recognition accuracy, high leakage rate, high false recognition rate, poor real-time performance, etc., and to achieve high-precision real-time detection of common distracted driving behaviours (mobile phone use, smoking, drinking), this paper proposes a driver distracted driving behaviour recognition algorithm based on YOLOv5. Firstly, to address the problem of poor real-time identification, the computational and parametric quantities of the network are reduced by introducing a lightweight network, Ghostnet. Secondly, the use of GSConv reduces the complexity of the algorithm and ensures that there is a balance between the recognition speed and accuracy of the algorithm. Then, for the problem of missed and misidentified cigarettes during the detection process, the Soft-NMS algorithm is used to reduce the problems of missed and false detection of cigarettes without changing the computational complexity. Finally, in order to better detect the target of interest, the CBAM is utilised to enhance the algorithm’s attention to the target of interest. The experiments show that on the homemade distracted driving behaviour dataset, the improved YOLOv5 model improves the mAP@0.5 of the YOLOv5s by 1.5 percentage points, while the computational volume is reduced by 7.6 GFLOPs, which improves the accuracy of distracted driving behaviour recognition and ensures the real-time performance of the detection speed.

An Algorithm for Distracted Driving Recognition Based on Pose Features and an Improved KNN

“…Ruthuparna et al [19] developed a CNN model with only 9.5 MB of parameters that could effectively recognize distracted driving behaviors involving hand movements. Luo et al [20] introduced a lightweight backbone network, GhostNet and GSConv, and YOLOv5 to reduce network parameters and achieve faster and more accurate recognition of common distracted behaviors such as answering phone calls, smoking, and drinking water. Li et al [21] combined visual transformers (VITs) and convolutional neural networks (CNNs) to more fully capture local and global image features.…”