Visual Inspection Improvement of Engine Components Using Deep Learning with Pre-processed Dataset Augmentation: Case Study - Steven Salim; Sandy Wiratama; Alfan Sarifudin; Eka Prita Yuliatin (Automotive Experiences, 2025)
Lighting instability, sharp shadows, and visual disturbances caused by mechanical vibrations are significant challenges in the application of computer vision-based visual inspection systems in automotive industrial environments. This study aims to enhance the accuracy and robustness of the YOLOv8 object detection model for detecting machine component completeness by applying an adaptive pre-processing strategy. The techniques employed include grayscale conversion, brightness adjustment, and blurring to simulate common visual conditions encountered in real-world production processes. The model was trained using 1,281 instances from 52 component classes and evaluated based on the metrics of precision, recall, mAP@50, and mAP@50”“95. The results show an average precision of 0.971, a recall of 0.990, and mAP@50 of 0.991, with spatial variation reflected in the standard deviation of mAP@50”“95 of 0.149. The pre-processing technique improves the detection precision of shape-based components by up to 19% and colour-based components by up to 31%. Testing on ten appearance variations showed 100% detection accuracy with no misclassification, indicating the model’s generalizability to data in the training distribution. These findings confirm that visual modification of training data significantly improves the reliability and efficiency of the YOLOv8-based automated inspection system. Further implications include reduced human intervention, accelerated production flow, and optimization of operational energy consumption through faster and more accurate detection. Therefore, this system contributes to energy-efficient and sustainable innovative manufacturing practices.