Optimizing Defect Detection on Glossy and Curved Surfaces Using Deep Learning and Advanced Imaging Systems

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 8; p. 2449
• Main Authors: Yoon, Joung-Hwan, Okwuosa, Chibuzo Nwabufo, Aronwora, Nnamdi Chukwunweike, Hur, Jang-Wook
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13.04.2025; MDPI
• Subjects: Accuracy; Algorithms; Artificial intelligence; Automation; Brain cancer; Cameras; Classification; Computational linguistics; Computer vision; convolutional neural network; COVID-19; curved surface; Deep learning; Defects; Design; Dijkstra, Edsger W; Dijkstra’s algorithm; Flexibility; glossy surface; Industry 4.0; Inspections; Language processing; Manufacturing; Medical imaging equipment; Methods; Natural language interfaces; Neural networks; Optimization techniques; ResNet-50; Robotics; Tumors; VGG-16; Vision systems; curved surface; fault classification; ResNet-50; VGG-16; convolutional neural network; fault detection; glossy surface; Dijkstra’s algorithm
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25082449

The industrial application of artificial intelligence (AI) has witnessed outstanding adoption due to its robust efficiency in recent times. Image fault detection and classification have also been implemented industrially for product defect detection, as well as for maintaining standards and optimizing processes using AI. However, there are deep concerns regarding the latency in the performance of AI for fault detection in glossy and curved surface products, due to their nature and reflective surfaces, which hinder the adequate capturing of defective areas using traditional cameras. Consequently, this study presents an enhanced method for curvy and glossy surface image data collection using a Basler vision camera with specialized lighting and KEYENCE displacement sensors, which are used to train deep learning models. Our approach employed image data generated from normal and two defect conditions to train eight deep learning algorithms: four custom convolutional neural networks (CNNs), two variations of VGG-16, and two variations of ResNet-50. The objective was to develop a computationally robust and efficient model by deploying global assessment metrics as evaluation criteria. Our results indicate that a variation of ResNet-50, ResNet-50224, demonstrated the best overall efficiency, achieving an accuracy of 97.97%, a loss of 0.1030, and an average training step time of 839 milliseconds. However, in terms of computational efficiency, it was outperformed by one of the custom CNN models, CNN6-240, which achieved an accuracy of 95.08%, a loss of 0.2753, and an average step time of 94 milliseconds, making CNN6-240 a viable option for computational resource-sensitive environments.