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 super-resolution images and low-resolution images, the mAP can reach 96.5%, which is 25.6% higher than the low-resolution image recognition rate.

As an important forest product, particleboard can greatly save forestry resources and promote low-carbon development by reusing wood processing residues. The size of the entire particleboard is large, and there are problems with less image feature information and blurred defect outlines when obtaining the particleboard images. The super-resolution reconstruction technology can improve the quality of the particleboard surface images, making the defects clearer. In this study, the super-resolution dense attention generative adversarial network (SRDAGAN) model was improved to solve the problem that the super-resolution generative adversarial network (SRGAN) reconstructed image would produce artifacts and its performance needed to be improved. The Batch Normalization (BN) layer was removed, the convolutional block attention module (CBAM) was optimized to construct the dense block, and the dense blocks were constructed via a densely skip connection. Then, the corresponding 52,400 image blocks with high resolution and low resolution were trained, verified, and tested according to the ratio of 3:1:1. The model was comprehensively evaluated from the effect of image reconstruction and the three indexes of PSNR, SSIM, and LPIPS. It was found that compared with BICUBIC, SRGAN, and SWINIR, the PSNR index of SRDAGAN increased by 4.88 dB, 3.25 dB, and 2.68 dB, respectively; SSIM increased by 0.0507, 0.1122, and 0.0648, respectively; and LPIPS improved by 0.1948, 0.1065, and 0.0639, respectively. The reconstructed images not only had a clearer texture, but also had a more realistic expression of various features, and the performance of the model had been greatly improved. At the same time, this study also emphatically discussed the image reconstruction effect with defects. The result showed that the SRDAGAN proposed in this study can complete the super-resolution reconstruction of the particleboard images with high quality. In the future, it can also be further combined with defect detection for the actual production to improve the quality of forestry products and increase economic benefits.

With the global population surge, the consumption of nonrenewable resources and pollution emissions have reached an alarming level. Engineered bamboo is widely used in construction, mechanical and electrical product packaging, and other industries. Its main damage is the material fracture caused by the expansion of initial cracks. In order to accurately detect the length of crack propagation, digital image correlation technology can be used for calculation. At present, the traditional interpolation method is still used in the reconstruction of engineered bamboo speckle images for digital correlation technology, and the performance is relatively lagging. Therefore, this paper proposes a super-resolution reconstruction method of engineering-bamboo speckle images based on an attention-dense residual network. In this study, the residual network is improved by removing the BN layer, using the $L_1$ loss function, introducing the attention model, and designing an attention-intensive residual block. An image super-resolution model based on the attention-dense residual network is proposed. Finally, the objective evaluation indexes PSNR and SSIM and subjective evaluation index MOS were used to evaluate the performance of the model. The ADRN method was 29.19 dB, 0.938, and 3.19 points in PSNR, SSIM, and MOS values. Compared to the traditional BICUBIC B-spline interpolation method, the speckle images reconstructed by this model increased by 8.55 dB, 0.323, and 1.43 points, respectively. Compared to the SRResNet method, the speckle images reconstructed by this model were increased by 4.53 dB, 0.111, and 0.14 points, respectively. The reconstructed speckle images of engineered bamboo were clearer, and the image features were more obvious, which could better identify the tip crack position of the engineered bamboo. The results show that the super-resolution reconstruction effect of engineered-bamboo speckle images can be effectively improved by adding the attention mechanism to the residual network. This method has great application value.

As a high-quality forestry product with excellent performance, particleboard is widely used in many fields and has great market potential. At present, it is a research difficulty to accurately identify small, fine and different shapes of defects on the whole particleboard through machine vision technology. The application of image super-resolution reconstruction technology on particleboard can improve the surface image quality of particleboard, which is conducive to the subsequent improvement of defect detection accuracy. In this study, super-resolution dense attention generative adversarial network (SRDAGAN) model was improved to solve the problem that super-resolution generative adversarial network (SRGAN) reconstructed image would produce artifacts and its performance needed to be improved. The Batch Normalization (BN) layer was removed, the convolutional block attention module (CBAM) was optimized to construct dense block, and the dense blocks were constructed by densely skip connection. Then, the corresponding 52400 image blocks with high resolution and low resolution were trained, verified and tested according to the ratio of 3:1:1. The model was comprehensively evaluated from the effect of image reconstruction and the two indexes of PSNR and SSIM. It was found that compared with BICUBIC and SRGAN, PSNR index of SRDAGAN increased by 4.88dB and 3.25dB respectively, and SSIM increased by 0.0507 and 0.1122 respectively. The reconstructed images not only had clearer texture, but also had more realistic expression of various features, and the performance of the model had been greatly improved. At the same time, this study also emphatically discussed on the image reconstruction effect with defects. The result showed that the SRDAGAN proposed in this study can complete the super-resolution reconstruction of particleboard images with high quality. In the future, it can also be further combined with defect detection for actual production to improve the quality of forestry products and increase economic benefits.