Penerapan Algoritma YOLOV5 Berbasis Computer Vision Untuk Pemantauan Real Time Child Part Assembly Bumper Pada Industri Otomotif
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Abby Muhammad Titan
*
Universitas Pelita Bangsa, Bekasi,
Indonesia
- Dendy K. Pramudito Universitas Pelita Bangsa, Bekasi, Indonesia
- Muhamad Fatchan Universitas Pelita Bangsa, Bekasi, Indonesia (*) Corresponding Author
Abstract
The assembly process of small-scale components, widely known as the bumper child part assembly stage in the automotive manufacturing industry, is highly vulnerable to human error and daily quality inspection inconsistencies. This vulnerability is primarily driven by excessive operational reliance on manual visual inspection conducted by human operators over extended periods. Such dependency frequently leads to defective products escaping to subsequent production lines, ultimately causing financial losses for the company. To address this critical issue, this study aims to design and implement an intelligent real-time monitoring system based on computer vision technology and the You Only Look Once version 5 (YOLOv5) deep learning algorithm to automatically detect anomalies in vehicle bumper component assembly. The image preprocessing stage is comprehensively performed using the OpenCV library and matrix manipulation to normalize variations in artificial lighting intensity on the production floor. The testbed in this study is focused on an assembly workstation equipped with ring illumination and a controlled conveyor speed to ensure visual data validity. This artificial intelligence system is then integrated into an interactive web monitoring interface that provides instantaneous feedback along with automated inspection logging into a database. Empirical test results in an actual production environment demonstrate that the system achieves an average detection accuracy of 98.6%, a precision of 98.9%, a recall of 99.8%, and an F1-score of 99.3%. The inference time is remarkably short at 12.5 milliseconds, enabling a processing speed of up to 65 frames per second. The principal contribution of this research lies in the development of an integrated inspection framework that synergizes a lightweight YOLOv5 detection model with a real-time Fatigue Analytics instrument and automated database logging. This integrated solution functions not only as a passive visual quality verifier but also as an empirical ergonomics diagnostic tool that suppresses component defect rates below two percent in actual manufacturing environments.
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