Machine Vision Based System for Automated Sorting System for Wire Bundle Quality Inspection

Ensuring reliable electrical continuity in wire- harness assemblies requires rapid detection and remediation of unintended gaps. We present a real-time machine-vision inspection pipeline built on the YOLOv5 object detector to automatically pinpoint physical discontinuities between jumper wires on a conveyor line. Drawing from a bespoke image corpus of annotated cluttered harnesses, our system trains a convolutional network to output tight ”gap” bounding boxes with confidence scores. Benchmarked on held-out frames, it sustains 87% detection precision and 82% recall, amid overlapping cables and variable illumination. Case studies illustrate how early gap flagging prevents glue over-application, reduces material waste, and offloads manual QA. We analyze failure modes—tiny oc- clusions and extreme wire crossings—and propose enhancements including multi-angle 3D capture and closed-loop integration with robotic applicators. Our results demonstrate that a lightweight YOLO-based approach can streamline wire assembly quality control and pave the way for fully automated glue-dispensing workflows.