YOLO-L: A High-Precision Model for Defect Detection in Lattice Structures

High-performance lattice structures produced through powder bed fusion-laser beam exhibit high specific strength and energy absorption capabilities. However, a significant deviation exists between the mechanical properties, service life of lattice structures, and design expectations. This deviation arises from the intense interaction between the laser and powder, which leads to the formation of numerous defects within the lattice structure. To address these issues, this paper proposes a high-performance defect detection model for metal lattice structures based on YOLOv4, called YOLO-Lattice (YOLO-L). The main objectives of this paper are as follows: (1) utilize computed tomography to construct datasets of the diamond lattice and body-centered cubic lattice structures; (2) in the backbone network of YOLOv4, employ deformable convolution to enhance the feature extraction capability of the model for small-scale defects; (3) adopt a dual-attention mechanism to suppress invalid feature information and amplify the distinction between defect and background regions; and (4) implement a channel pruning strategy to eliminate channels carrying less feature information, thereby improving the inference speed of the model. The experimental results on the diamond lattice structure dataset demonstrate that the mean average precision of the YOLO-L model increased from 96.98% to 98.8% (with an intersection over union of 0.5), and the inference speed decreased from 51.3 ms to 32.5 ms when compared to YOLOv4. Thus, the YOLO-L model can be effectively used to detect defects in metal lattice structures.