Automated Surface Quality Control via Deep Visual Inspection in Manufacturing: A ResNet50 Implementation Case Study

Effective quality assurance in industrial manufacturing hinges on the accurate detection and classification of surface anomalies.Conventional manual visual inspection methods are inherently limited by operator subjectivity and fatigue, leading to inconsistent results and potential errors.To minimize these limitations, automated inspection systems utilizing Artificial Intelligence (AI), specifically Convolutional Neural Networks (CNNs), have been implemented.Among the widely adopted CNN architectures for this application are ResNet50, MobileNetV3, and EfficientNet-B0.The ResNet50 which is characterized by its deep residual learning framework exhibits superior classification accuracy, rendering it particularly suitable for identifying nuanced and complex defects.The second one, MobileNetV3, engineered for low-latency inference on mobile or resource-constrained hardware, offers accelerated processing but compromises slightly on accuracy.The last one, EfficientNet-B0, provides a balanced trade-off between accuracy and computational efficiency, yet its performance is surpassed by ResNet50 when classifying intricate defect patterns.Our findings confirm that ResNet50 demonstrates superior performance, especially for high-fidelity detection tasks involving defect types like cracks, stains, and deformations.Although MobileNetV3 and EfficientNet-B0 serve well in real-time or lightweight system deployments, ResNet50 remains the optimal choice for industrial contexts where maximizing detection accuracy is paramount, owing to its robustness in modeling complex defect characteristics and delivering reliable classifications.