Nondestructive inspection method of welding rate for heat sink fins with complex structure via infrared thermography principle and deep learning method

This paper proposes a non-destructive defect inspection technique based on infrared thermal imaging and improved YOLOv5 algorithm to achieve accurate inspection of finned heat sink welding rate, which can be used to improve the quality of finned heat sink defect inspection. Using fin heat dissipation and infrared thermal imaging principles, samples with heat sink quality problems can be efficiently identified, guaranteeing high quality acquisition of samples. By improving the YOLOv5 algorithm applying BiFPN (Bidirectional Feature Pyramid Network) feature fusion method to improve the neck structure of the original network structure, simplify the convolution nodes, add the CBAM (Convolutional Block Attention Module) module to improve the feature extraction capability and inspection efficiency, and optimise the original loss function and prediction frame screening method in order to improve the infrared target inspection accuracy. The experiment verifies that the improved model can effectively detect defective targets under different backgrounds, and the average inspection accuracy (mAP) can reach 90.3 %, which makes the model more adaptable and reliable in practical applications compared with traditional target inspection algorithms.