Surface Defect Detection of Heat Sink Based on Lightweight Fully Convolutional Network

Abstract

The surface defect detection plays an important role in industrial production, and directly affects production efficiency and product quality. In this paper, we focus on the surface defect detection of heat sink and propose a mothed based on a Ghost-SE light U-Net (GSLU-Net). The GSLU-Net is a novel combination of the lightweight convolution module, self-attention mechanism, and fully convolutional network. It has a symmetrical architecture inspired by the U-Net. By introducing the Ghost module which can generate feature maps with cheap operations, we reduce the computation cost while maintaining high accuracy. The Ghost module uses an ordinary convolution with small kernels to obtain original feature maps, then a depth-wise convolution to generate more feature maps. The SE block is also introduced to improve the representational power of the network and further improve accuracy. It can adaptively recalibrate channel-wise feature responses at a slight additional computational cost. Fewer down-sampling layers and more skip connections enable this network to retain more location information and have stronger detection capabilities for tiny targets than previous fully convolutional networks. The ablation experiments validate that our improvements have played their due role. Then the comparative experiments demonstrate that our network outperforms the state-of-the-art fully convolutional networks. The GSLU-Net reaches an accuracy of 97.96% at a speed of 14.115ms per image on the dataset of heat sink surface defect, achieving a balance of efficiency and accuracy. The GSLU-Net also outperforms other networks on open datasets, further demonstrating the superiority of the GSLU-Net.