Journal of Electrical Engineering and Automation

To address the problems of low efficiency, limited viewing angle, and high safety risks in traditional manual inspection of cage stranding machine spools, this paper designs and implements an intelligent monitoring and automatic control system based on machine vision. The system uses high frame rate industrial cameras with ring light sources for image acquisition, and deploys optimized YOLOv11 object detection algorithm on RK3588 edge computing platform to achieve real-time detection of various spool states including orderly winding, disorderly winding, wire clamping, and wire breaking. Through model compression and INT8 quantization techniques, the system achieves real-time inference at the edge while maintaining detection accuracy. Test results show that the detection accuracy for various states exceeds 92%, with wire breaking detection accuracy reaching 98.2%. The system can achieve 24-hour unmanned monitoring, effectively improving production efficiency and reducing safety risks, providing reliable technical support for industrial automated production.

Machine vision; Cage stranding machine; Intelligent monitoring; Edge computing