YOLO-MPID: an improved YOLOv5 network for in situ detection of laser cladding melt pool states.

Abstract

High-speed laser cladding is a critical surface modification technique, where the melt pool state critically determines the quality of the cladding layer. To address the challenges of inaccurate monitoring and the lack of real-time feedback control during the cladding process, this study proposes an improved YOLOv5-based object detection model, named YOLO-MPID. The model incorporates the Squeeze-and-Excitation (SE) module and the Convolutional Block Attention Module (CBAM) to enhance the extraction of complex edge structures and semantic features in melt pool images. Ablation studies were conducted to evaluate the individual and combined effects of SE and CBAM on detection performance. Comparative experiments under varying laser power conditions demonstrated that YOLO-MPID achieves superior robustness and accuracy compared to the baseline YOLOv5 and other mainstream detection algorithms. Experimental results show that the proposed model achieves a mean Average Precision (mAP at 0.5) of 97.42% and a real-time inference speed of 121.71 FPS. Visual analysis further supports the quantitative findings. In summary, YOLO-MPID provides an effective solution for real-time melt pool state detection and quality control in practical industrial scenarios, offering robust technical support for advancements in image science and optical process control.