Automated Defect Detection System for Automobile Accessory Manufacturing Using YOLOv5

The rapid expansion of the automobile industry has brought to light critical safety issues, requiring for extreme caution throughout the assembly and manufacturing of vehicles. Defect identification in automotive components has therefore gained significant importance. This paper offers a novel method of deep learning for defect detection in auto accessories, moving from manual inspection to machine vision approaches. The core components of this system comprise YOLOv5, web cameras, and Raspberry Pi boards. The primary aim is to seamlessly detect and categorize defects within the manufactured automobile accessory products during the production phase. The YOLOv5 algorithm serves as the foundation for defect identification in automobile spare parts. Unlike employing other models for detecting the spare parts this research work relies on the implementation of YOLOv5 due to its suitability for real-time object detection tasks. Additionally, as YOLOv5 allows for the modification of accuracy criteria based on application requirements unlike predefined rates associated with other models. It is significant that the implementation of YOLOv5's advanced computer vision capabilities, the system ensures precise differentiation between defective and non-defective items, thereby guaranteeing that only top-quality products meet the stringent standards for distribution. Moreover, our system seamlessly transmits the gathered data to an Internet of Things (IoT) platform, leveraging Raspberry Pi boards and web cameras for integration. This IoT integration empowers real-time monitoring and comprehensive data analysis. Our technology significantly enhances product quality, optimizes manufacturing processes, and improves the company's competitive advantage in the automobile accessory market by enabling rapid decision-making and prompt corrective action, thus enhancing overall product quality, manufacturing efficiency, and the company's competitive edge in the automotive accessory market.