Efficient Detection Model of Illegal Driving Behavior in Two-Wheeled Vehicles

The intelligent detection of illicit driving behaviors exhibited by two-wheeled vehicles, encompassing electric two-wheeled vehicles, motorcycles, and bicy-cles, constitutes a pivotal facet in developing a contemporary intelligent traffic monitoring system. However, prevailing challenges confront intelligent detec-tion in this domain, manifesting in two principal predicaments: (1) an absence of pertinent open-source datasets and (2) suboptimal accuracy and swiftness in discerning illicit driving behavior of two-wheelers within the prevailing object detection model. Hence, this study focuses on two focal points: (1) constructing the two-wheeled vehicle illegal driving behavior detection (TIDBD) dataset coupled with annotating 10 driving states, and (2) the proposition of an effica-cious detection model, YOLOv8_VanillaBlock, tailored for detecting illegal driving behavior in two-wheeled vehicles. We experimentally compared YOLOv8_VanillaBlock with the original YOLOv8 using the TIDBD dataset employing evaluation metrics such as floating point operations (FLOPs), mean average precision (mAP), and GPU inference time. The outcomes indicate that YOLOv8_VanillaBlock yields superior detection results.