Intelligent Wood Inspection Approach Utilizing Enhanced Swin Transformer

Abstract

Wood diameter needs to be measured in the process of production, sales and import and export. In order to solve the problem that it is difficult to accurately measure the densely stacked and irregularly arranged vehicle wood manually, this paper proposes a timber segmentation methodology that leverages a Swin Transformer model mechanism to enhance the performance of the target detection model. The method automatically learns and calculates distinct regions in the input image, assigning varying weights to different sizes and shapes of wood. This approach achieves finer detection of densely stacked logs, thereby promoting intelligent inspection and enhancing inspection efficiency.This study optimizes the backbone network by refining its modules and incorporating the operation of the log-space bias module. Additionally, improvements are made to the feature fusion network and loss function to further enhance network performance. The instance segmentation model parameters are also modified, encompassing multi-scale training, an increased number of training samples, improved image input size, and effective data widening techniques, all of which enhance log measurement accuracy and resolve the issue of partially occluded logs.This study conducts multiple control experiments to evaluate various scale metrics, such as mean average precision (mAP), log true detection rate, false detection rate, as well as comparing the root count and volume of logs through prediction. The experiments demonstrate that the mAP of this methodology reaches 0.685, and the true detection rate reaches 0.96 when compared with mainstream neural networks of similar scale, highlighting the advantages of this paper’s approach in wood segmentation detection. The model exhibits a strong detection effect on dense wood, effectively overcoming occlusion challenges, leading to more accurate measurement data. Moreover, the algorithm demonstrates robustness and migration ability, rendering it highly applicable to the task of detecting and segmenting dense wood of all sizes.

Keywords

• Dense wood detection
• Swin transformer
• obscured targets
• deep learning