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In the process of tool production, due to personnel, machinery, environment and other reasons, the surface of the tool will appear a variety of defects, such as scratches, impact pits, shedding and edge break. These defects will seriously affect the quality and appearance of the tool, for the tool defect detection, the current main method is manual visual inspection, manual detection method efficiency and accuracy are low. In order to solve the above problems, an automatic tool defect detection and classification algorithm is proposed. For tool image preprocessing, a noise reduction method based on bilateral filtering and contrast enhancement algorithm based on difference are proposed. For tool defect detection task, a defect detection algorithm based on image difference is proposed. A classification algorithm based on SVM is proposed for defect classification task. The SVM classifier is trained by extracting the shape, texture and other features of the defect area. Finally, the experiment of the proposed defect detection and classification algorithm is carried out, and the results show that the defect detection rate of the algorithm is 99.7%, and the classification accuracy is 95%. The algorithm can meet the needs of industry and replace the manual to realize the automation and high efficiency detection of tool defects.