Electric and Motorbike Violation and Overload Dataset

The current transportation industry faces frequent violations by electric and motorbikes, exacerbating urban traffic safety hazards. Existing monitoring systems often rely on manual methods or simple image recognition, with limitations of low recognition rate and poor real-time performance. This dataset aims to enhance the accuracy and robustness of object detection models in identifying electric and motorbike violations with high-quality annotated data. Data is collected using high-resolution cameras at major urban traffic intersections, ensuring diverse traffic scenes. For quality control, multiple rounds of annotation and consistency checks are conducted to ensure data accuracy and reliability. Data is stored in JPG format and organized in a structured manner, facilitating subsequent processing and analysis. The dataset has high annotation precision and consistency, with an annotation accuracy rate of over 95%, addressing the low recognition rates of traditional methods. By incorporating advanced data augmentation techniques, model performance can be improved by over 20%, offering significant application value and supporting the development of intelligent transportation systems.