GBCD-YOLO: A High-Precision and Real-Time Lightweight Model for Wood Defect Detection

Abstract

With the advancement of the wood processing industry, the demand for the detection of surface defects in wood has become increasingly urgent. The application of automated production technology has enhanced the efficiency and precision of wood processing, which can significantly impact product quality and competitiveness. However, current methods for detecting surface defects in wood suffer from issues such as low detection accuracy, high computational complexity, and poor real-time performance. In response to these challenges, this paper proposes a high-precision, lightweight, real-time wood surface defect detection method based on YOLO(GBCD-YOLO) model. Firstly, the Ghost Bottleneck is introduced to improve the computational efficiency and inference speed of deep neural networks. Furthermore, the BiFormer is incorporated in the neck to enhance the performance of natural language processing tasks. Simultaneously, CARAFE is utilized as an upsampling replacement to enhance perceptual and capture abilities for details. In addition, the Dynamic Head is introduced to enhance the method’s flexibility and generalization ability, and the loss function is replaced with complete intersection over union (CIoU). The proposed method was evaluated using an optimized dataset and the YOLOv5s model was chosen as the baseline. The experimental results show that compared with the original YOLOv5s, the mAP (0.5) has been improved by 13.45%, reaching 88.72%. The mAP (0.5:0.95) increased by 11.95%, and FPS increased by 6.25%. In addition, the parameter of the improved model has been reduced by 15.49%. These results indicate that the proposed GBCD-YOLO improves the real-time detection performance of wood surface defects.

Keywords

• Small target detection
• wood defect
• deep learning
• transformer
• YOLOv5