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In the production process of aluminum profiles, due to various factors, defects such as bumps, scratches, and dirty spot will occur on the surface of aluminum profiles. These defects will seriously affect the quality of aluminum profiles. At present, manual inspection is mainly used. Because the surface of the aluminum profile itself contains texture and is not highly distinguishable from defects, the traditional manual visual inspection is very laborious and the quality inspection effect is difficult to control. In order to solve the above problems, the surface defects of aluminum profiles are used as research objects, and Gaussian-yolov3 is used as the target detection network. For strip defects in aluminum profiles, deformable convolution technology is used to enhance the adaptability of convolution. For small defect detection, we used dense connection technologyEnhance extended data. By comparing Faster R-CNN and SSD experiments, the results show that the accuracy of the detection method based on Gaussian-yolov3 can reach 96%, the detection speed can meet the real-time requirements, and it has strong practicability.