Aiming at the problems of low visual recognition rate and serious human resource consumption of metal injection molding（MIM）industrial production parts due to the fine defects

a defect detection and recognition method based on convolution neural network（CNN） is designed.The problem of low recognition rate caused by the lack of training data is solved by using transfer learning.A lightweight feature reuse network model is proposed to detect the defects of small module gears produced by MIM process.The experimental results show that the accuracy rate of the training model in MIM gear defect detection is more than 98 %

which can realize the rapid and accurate detection of parts.The detection and recognition technology can be applied to similar parts manufacturing industry to complete the classification and detection tasks

which has important research value and practical significance in industry.