At present, there may be some problems in the production process of spoon, such as the lack of material on the surface of spoon. In order to effectively detect the surface defects of spoon, a defect detection method based on improved YOLO V3 model is proposed in this paper. Firstly, the output layers of the second and third residual blocks in the backbone network Darknet-53 are selected to build the feature pyramid network, which shortens the transmission path of feature information. In this case, we can better retain the feature information of small target defects. Secondly, the anchor boxes is adjusted to strengthen the ability of the model for small target defects detection. We test the proposed method on one spoon defect dataset, which is collected from the real-world industry manufactory scenario. The results show that the average precision of our algorithm reaches at 95.14%, which is better than the conventional YOLO V3 algorithm by 9.35%. Meanwhile, our algorithm is 9.12% faster than YOLO V3 with a 32.3 fps detection speed, demonstrating its efficiency and effectiveness for spoon defect detection.