Application of Image Recognition Based on Machine Learning in Automated Inspection

With the development of Industrial Automation, image recognition technology has been widely used in automated inspection. Based on the current machine learning methods, the paper designs and implements an effective automatic inspection system: data acquisition and preprocessing, feature extraction, model training and optimization, prediction and reasoning, and system feedback. Using the convolutional neural network and transfer learning model of ResNet-50, the comparative experiments with the traditional support vector machine and random forest algorithm show that the deep learning method has prominent advantages in key performance indicators such as accuracy, precision, recall, and F1 score, especially the best performance of ResNet-50 model in the test. In addition, the introduction of the system feedback mechanism enables the inspection system to have the self-optimization ability and continuous improvement, further improving the overall performance and adaptability of the system. Experimental results prove that the image recognition technology based on machine learning is effective in automated inspection, which can provide reliable technical support for actual industrial inspection.