Automotive fuse & relay box plug-in modules assembly correctness detection system based on machine vision

The automotive fuse and relay box is vital for electrical safety and reliability, demanding stringent quality control before leaving the factory. However, existing methods face limitations such as light interference, inability to detect non-fuse plug-in modules, lack of worker-friendly interfaces, insufficient data recording features, and a lack of comparative diagnostic capabilities for detection results. To address these issues, an artificial intelligence (AI)-powered automotive fuse and relay box assembly correctness detection system based on machine vision is proposed. This system incorporates a closed image acquisition setup, advanced machine vision techniques, and My Structured Query Language (MySQL) database operations for efficient data management. A comprehensive detection rule-setting subsystem, developed with Python Qt 5 (PyQt5) graphical user interface (GUI), integrates classification detection, similarity detection, color detection, and text recognition, allowing users to easily create detection rules. Additionally, a PyQt5-based template selection subsystem further streamlines template identification for various scenarios. The detection system combines these four methods with an object detection method for real-time, accurate assembly verification. The core You Only Look Once version 11 extra-large (YOLOx) model provides fast and precise localization, while supplementary modules—Residual Neural Network with 18 layers for classification detection, Siamese network-based similarity detection, binary character recognition, and color detection—work synergistically to enhance detection robustness and accuracy. The system achieves an average detection time of 0.141 s per module for correct assemblies and 1.398 s for faulty assemblies. Demonstrating 99.9 % accuracy, high adaptability, and efficient detection, the system is highly suitable for large-scale, real-world production environments.