LE-MSFE-DDNet: a defect detection network based on low-light enhancement and multi-scale feature extraction

Surface defect detection of industrial products has become a promising area of research. Among the existing defect detection algorithms, most of the CNN-based methods can achieve the task of defect detection under ideal experimental conditions. However, the accuracy of defect detection is easily affected by the different lighting conditions of the environment and the inconsistency of the defect scale. Therefore, general deep learning methods have difficulties in solving the problem of defect detection in complex scenes. In this paper, a defect detection network based on low-light enhancement and multi-scale feature extraction (LE-MSFE-DDNet) is proposed. There are two blocks in the proposed network, including a low-light enhancement block and a SE-FP block. In the low-light enhancement block, the deep network is applied to enhance light adaptation of the deconstructed low-light feature map in our network. The influence of illumination inconsistency is weakened by the introduction of this block. In the SE-FP block, the dependencies between different channels are combined with the multi-scale feature extraction. The defects with different scales are accurately located through the combination of this block and our network. In addition, a Fine Cans Defect dataset based on the surface of fine cans is collected by this paper to verify the feasibility of the proposed network. The proposed model is compared with the state-of-the-art object detection network and the proposed method achieves 94.3% average accuracy on the Fine Cans Defect dataset. The experimental results show that the proposed method outperforms the state-of-the-art method for surface defect detection.

Automated summary

This paper proposes a defect detection network based on low-light enhancement and multi-scale feature extraction, which introduces two blocks for low-light enhancement and combining channel dependencies for multi-scale feature extraction. This network can accurately locate defects of different scales in complex scenes, outperforming the state-of-the-art method for surface defect detection.