This study aims to enhance the accuracy and efficiency of copper burr detection for large motor rotor wires used in industrial environment. The problems of optical illumination, variety of burr species, and low recognition rate of the detection system are considered. To solve these problems, one kind of defect feature extraction method based on the copper burr growth region of the motor is proposed. The burr defect automatic recognition system is consistedof hardware module and image processing module. Based on analysis of burr features, the standard image of the detected objectis obtainedby using the maskbased image optimization algorithm. Then, the image region to be detected and segmented by morphological algorithm is constructed. Finally, the judgement results through classification algorithm and threshold denoising scheme for various burrs are achieved.The automatic detection of the copper burr defect is realized. Experimental results show that the algorithm can detect burr defects quickly and accurately. It has high robustness for detectingthe burrs generated during copper processing. The detection rate, leakage rate and false detection rate are close to 98%, 0%, and 1471%,respectively. The proposed method can meet the requirements of industrial test.