Small Defect Detection Algorithm of Particle Board Surface Based on Improved YOLOv5s

An improved algorithm YOLOv5s-ATG for defecting particle board defects, based on YOLOv5s, is proposed to address the problem of poor precision in small target detection of particle board defect detection at present. To overcome the issue of particle board defects with small targets and large-scale changes, the original detector head is combined with the adaptive spatial feature fusion (ASFF) network to obtain better feature fusion. Transformer module is introduced into the backbone network, which uses a multi head self-attention mechanism to capture global spatial relationships and enhance the feature extraction capability of the network. For balancing the accuracy and complexity of the model, the Ghostv2 module is added to the backbone and neck of the network to improve the real-time performance of the algorithm. The experimental results show that the mean average precision (mAP) of the improved algorithm in the actual particle board defect data set can reach 0.901, which is 0.046 higher than the original model; for small target defect Gluespots, mAP is increased by 0.138. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.