Detecting defects in fused deposition modeling based on improved YOLO v4

Abstract

Fused deposition modeling comes with many conveniences for the manufacturing industry, but many defects tend to appear in actual production due to the problems of the FDM mechanism itself. Although some deep learning-based object detection models show excellent performance in detecting defects in the additive manufacturing process, their detection efficiency is relatively low, and they are prone to drawbacks in the face of large numbers of defects. In this paper, an improved model based on the YOLO v4 network structure is developed. We lightweight the model and modify its loss function to achieve better performance. Experimental results show that the improved model, MobileNetV2-YOLO v4, achieves a mAP of 98.96% and an FPS of 50.8 after training, which obtains higher detection accuracy and faster detection speed than the original YOLO v4 algorithm model. Through testing, this improved model can accurately identify the location and information of target defects, which has great potential for real-time detection in the additive manufacturing process.