With the rapid growth of the global economy, transportation safety challenges have become increasingly prominent, resulting in substantial losses to life and property. Electric bikes (E-bikes) exhibit relatively high speeds and poor dynamic stability, leading to elevated driving risks, making accurate real-time E-bike detection a critical requirement for road safety and intelligent driving systems. Despite the unique significance of dedicated E-bike detection in Intelligent Transportation Systems (ITS), research on E-bike detection as a distinct category remains limited, compounded by a lack of open, complex-environment E-bike datasets. To address these gaps, we design a novel YOLO-DC model to improve the accuracy and real-time performance of E-bike detection in complex traffic environments. We enhance the YOLOv8s model with three targeted modifications: integrating Deformable Convolutional Network-v2 (DCNv2) into the backbone for improved spatial adaptability, embedding the Convolutional Block Attention Module (CBAM) in the neck for enhanced spatial-channel feature representation, and optimizing bounding box regression with a novel Hybrid Intersection over Union (HIoU) loss for small-object detection. To mitigate the scarcity of open E-bike data, we create the Electric Bikes Dataset (EBD), a curated dataset of over 1257 real-road images capturing E-bikes under diverse conditions (low light, occlusion, bad weather, tilted perspectives). The YOLO-DC model is validated on the EBD dataset and in real-world traffic scenarios, achieving a 12.4% improvement in mAP50 (mean Average Precision at IoU = 0.5) and an overall detection accuracy of 98.4% compared to the baseline YOLOv8s model. The YOLO-DC model delivers high real-time performance for E-bike detection and provides a scalable foundation for tracking in subsequent systems, with significant potential to advance intelligent vehicle perception systems and enhance ITS road safety capabilities.
Li et al. (Mon,) studied this question.