Lightweight Segmentation Method for Wood Panel Images Based on Improved DeepLabV3+

Accurate and efficient pixel-wise segmentation of wood panels is crucial for enabling machine vision technologies to optimize the sawing process. Traditional image segmentation algorithms often struggle with robustness and accuracy in complex industrial environments. To address these challenges, this paper proposes an improved DeepLabV3+-based segmentation algorithm for wood panel images. The model incorporates a lightweight MobileNetV3 backbone to enhance feature extraction, reducing the number of parameters and computational complexity while minimizing any trade-off in segmentation accuracy, thereby increasing the model's processing speed. Additionally, the introduction of a coordinate attention (CA) mechanism allows the model to better capture fine details and local features of the wood panels while suppressing interference from complex backgrounds. A novel feature fusion mechanism is also employed, combining shallow and deep network features to enhance the model's ability to capture edges and textures, leading to improved feature fusion across scales and boosting segmentation accuracy. The experimental results demonstrate that the improved DeepLabV3+ model not only achieves superior segmentation performance across various wood panel types but also significantly increases segmentation speed. Specifically, the model improves the mean intersection over union (MIoU) by 1.05% and boosts the processing speed by 59.2%, achieving a processing time of 0.184 s per image.