Real-Time Quality Inspection System Using Camera Vision and Machine Learning Algorithm: CNN

Guaranteeing product quality is an essential element of contemporary manufacturing, necessitating precise and effective inspection systems. This research introduces a real-time quality assessment system utilizing Convolutional Neural Networks (CNNs) to identify production flaws through high-resolution image data. The suggested system utilizes an improved Faster R-CNN model that boosts feature extraction, classification, and defect localization. The model undergoes training on a dataset containing six types of metal surface defects, attaining higher accuracy and computational efficiency than conventional machine vision methods. Experimental findings show that the enhanced Faster R-CNN reaches an accuracy of 98%, surpassing current methods like SVM and traditional CNNs. The results indicate that the suggested system can greatly enhance defect identification in industrial settings, minimizing manual inspection work and maintaining uniform product quality.