  In recent years, deep learning has made significant progress in wood panel
defect detection. However, there are still challenges such as low detection ,
slow detection speed, and difficulties in deploying embedded devices on wood
panel surfaces. To overcome these issues, we propose a lightweight wood panel
defect detection method called YOLOv5-LW, which incorporates attention
mechanisms and a feature fusion network.Firstly, to enhance the detection
capability of acceptable defects, we introduce the Multi-scale Bi-directional
Feature Pyramid Network (MBiFPN) as a feature fusion network. The MBiFPN
reduces feature loss, enriches local and detailed features, and improves the
model's detection capability for acceptable defects.Secondly, to achieve a
lightweight design, we reconstruct the ShuffleNetv2 network model as the
backbone network. This reconstruction reduces the number of parameters and
computational requirements while maintaining performance. We also introduce the
Stem Block and Spatial Pyramid Pooling Fast (SPPF) models to compensate for any
accuracy loss resulting from the lightweight design, ensuring the model's
detection capabilities remain intact while being computationally
efficient.Thirdly, we enhance the backbone network by incorporating Efficient
Channel Attention (ECA), which improves the network's focus on key information
relevant to defect detection. By attending to essential features, the model
becomes more proficient in accurately identifying and localizing defects.We
validate the proposed method using a self-developed wood panel defect
dataset.The experimental results demonstrate the effectiveness of the improved
YOLOv5-LW method. Compared to the original model, our approach achieves a
92.8\% accuracy rate, reduces the number of parameters by 27.78\%, compresses
computational volume by 41.25\%, improves detection inference speed by 10.16\%
