ICK-PANet: A Multiscale Driver Distraction Detection Network Based on Attention and Pyramid Convolution

In recent years, the number of deaths caused by traffic accidents has continued to rise. According to investigations, approximately one-fifth of accidents are caused by drivers being distracted. With the rapid development of convolutional neural networks (CNNs) in the field of computer vision, many researchers have developed CNN-based network models to recognize distracted driving actions. However, many models have too many parameters, making them unsuitable for deployment in actual vehicles. To address this issue, we propose a multiscale driver distraction detection network called ICK-PANet, which combines attention, lightweight incremental convolution kernels, and lightweight pyramid convolution to quickly and accurately identify driver distraction actions. First, ICK-PANet uses lightweight incremental convolution kernels to capture global information and driving action details effectively. Then, it introduces lightweight pyramid convolution and attention modules to extract multistage features, thereby expanding the network’s receptive field to improve the recognition ability of key features. Finally, it fuses multistage features to predict the results. ICK-PANet was experimentally evaluated on two public datasets: the American University in Cairo Distracted Driver (AUC) dataset and the StateFarms dataset (SFD) provided by the Kaggle competition platform. The AUC and SFD accuracies are 95.66% and 99.84%, respectively, which are higher than those achieved by many other state-of-the-art methods. ICK-PANet requires only 0.4M parameters, making it one of the most lightweight models currently available.