The work aims to study a real-time packaging defect detection method based on deep learning, in view of the problems such as complexity, considerable professional knowledge, poor generality, and difficulty in application under multi-objective and complex background of the current packaging defect detection methods based on artificial feature ex-traction in industrial automation production. In the case of small sample set, a defect detection method combining the In-ception-V3 image classification algorithm and YOLO-V3 target detection algorithm based on deep learning was proposed, and a complete online packaging defect detection system based on computer vision was designed. Experimental results showed that the recognition accuracy rate and variance of the proposed method were 99.49% and 0.000 050 6 respectively. The accuracy rate of using only Inception-V3 algorithm was 97.70% and its variance was 0.000 251. Compared with the general packaging defect detection method based on artificial feature extraction, the proposed method avoids the complex feature extraction process. Compared with the packaging defect detection only with image classification algorithm, the proposed method can obviously improve the accuracy and stability of packaging defect detection especially when the defect occupies a relatively small proportion, and performs well in complex detection background and multi-objective situation. At the same time, the defect detection system and detection method designed herein can be easily migrated to other similar online detection problems.