Lightweight aero-engine surface defect detection algorithm fused with attention mechanism

In response to the problems of large parameters, high network complexity, and low portability of aero-engine surface defect detection algorithms, a lightweight defect detection algorithm WGS-YOLO combined with attention mechanism based on YOLOv5s (you only look once version 5 small) was proposed. The algorithm utilized the ShuffleNet V2 network to construct the unit-reconstructed backbone network, reducing network complexity. The presented AW-CBAM attention mechanism (CBAM attention mechanism with adaptive adjustment of weights) was capable of simultaneously extracting channel attention features and spatial attention features, and dynamically adjusting the weight proportion of the two features through adaptive weight coefficients, which enabled the network to pay more attention to the global information of the input feature map, and apply it into the backbone network to enhance the network’s representation and generalization capabilities. A lightweight GS-C3 module was designed in the neck network by introducing depthwise separable convolution and Ghost convolution to achieve feature condensation of the input feature map, efficiently capturing important information in input features. The experimental results showed that the recognition precision of the WGS-YOLO algorithm reached 92.0%, 2.1% higher than the baseline. Meanwhile, the network parameters were reduced by 55.3%, and the computational load decreased by 57%. Therefore, the proposed algorithm can meet the design requirements of lightweight network and effectively detect major surface defects in aero-engines.