Lightweight irregular driving behavior detection algorithm based on star operation

To address the problems of high memory consumption, heavy computational burden, and difficulty deploying existing detection algorithms on edge devices, a lightweight approach for detecting abnormal driving behavior based on an updated YOLOv8 model has been presented. Firstly, the backbone network is replaced with the StarNet network, which has lower computational and memory access requirements. Second, utilizing an enhanced weighted bidirectional feature pyramid network with C2f blocks enhances the model's capability to capture multi-scale features swiftly. Finally, an asymmetric multi-level compression detection head is introduced to reduce the model size further. The experimental findings demonstrate that the suggested enhanced model achieves a 73.2% reduction in parameters, a 60.9% decrease in computational burden, and a 72.8% reduction in model size while retaining the same accuracy as the original YOLOv8 model. This significantly reduces hardware costs, demonstrating excellent lightweight effects.