Real-time detection and alerting system for dangerous electric bicycle driving behaviors based on YOLOv11 and K230 edge-AI SoC

Abstract

This paper proposes a real-time monitoring and warning system for dangerous electric bicycle driving behaviors based on the YOLOv11 model and the K230 edge computing platform. The system is designed for urban roads and campus traffic environments, where behaviors such as riding without helmets, illegal passenger carrying, and speeding frequently occur. Compared with traditional cloud-based monitoring methods, the proposed system can perform real-time detection on edge devices with lower deployment cost and lower latency. The system uses the K230 edge AI chip based on the dual-core RISC-V architecture [6] as the main hardware platform and deploys the lightweight YOLOv11 model for traffic violation detection. To improve detection performance for small targets such as helmets and partially occluded riders, the C3k2 and C2PSA modules were introduced into the model structure. In addition, multiple data augmentation methods were used during training to improve robustness under different lighting and weather conditions. To improve the accuracy of monocular speed estimation, this paper introduces a speed measurement method based on camera calibration and inverse perspective mapping (IPM). Compared with traditional pixel-displacement methods, the proposed method reduces the influence of perspective distortion and improves measurement accuracy without additional radar sensors. Furthermore, a multi-frame decision strategy based on temporal consistency is used to reduce false alarms caused by temporary occlusion or unstable detection results. Experimental results show that the INT8-quantized model achieved an inference speed of 22 FPS on the K230 platform, while maintaining 91.9% mAP@0.5. The average relative error of speed estimation was controlled within 5.3%. The proposed system combines object detection, speed estimation, and remote warning functions into a complete edge-side monitoring framework, providing a practical solution for intelligent traffic supervision.