Intelligent Detection Method for Surface Defects of Particleboard Based on Super-Resolution Reconstruction

作者：Haiyan Zhou, Haifei Xia, Chenlong Fan, Tianxiang Lan, Ying Liu, Yutu Yang, Yinxi Shen, Wei Yu · 发表于：Forests · 年份：2024 · DOI：10.3390/f15122196 · 被引用次数：10 · 研究领域：Advanced Image Processing Techniques、Industrial Vision Systems and Defect Detection、Remote Sensing and LiDAR Applications

To improve the intelligence level of particleboard inspection lines, machine vision and artificial intelligence technologies are combined to replace manual inspection with automatic detection. Aiming at the problem of missed detection and false detection on small defects due to the large surface width, complex texture and different surface defect shapes of particleboard, this paper introduces image super-resolution technology and proposes a super-resolution reconstruction model for particleboard images. Based on the Transformer network, this model incorporates an improved SRResNet (Super-Resolution Residual Network) backbone network in the deep feature extraction module to extract deep texture information. The shallow features extracted by conv 3 × 3 are then fused with features extracted by the Transformer, considering both local texture features and global feature information. This enhances image quality and makes defect details clearer. Through comparison with the traditional bicubic B-spline interpolation method, ESRGAN (Enhanced Super-Resolution Generative Adversarial Network), and SwinIR (Image Restoration Using Swin Transformer), the effectiveness of the particleboard super-resolution reconstruction model is verified using objective evaluation metrics including PSNR, SSIM, and LPIPS, demonstrating its ability to produce higher-quality images with more details and better visual characteristics. Finally, using the YOLOv8 model to compare defect detection rates between su...