Surface defect detection based on computer vision remains a challenging task due to the uneven illumination, low contrast and miscellaneous patterns of defects. Current methods usually present undesirable detection accuracy and lack adaptability for the various scenes. In the paper, the novel uneven illumination surface defects inspection (UISDI) method is proposed to address these issues. First, the multi-scale saliency detection (MSSD) method is proposed to construct a coarse defect map and obtain the corresponding background regions. Second, a novel background similarity prior-based intrinsic image decomposition model (BSIID) is applied to divide the defect image into a non-defective shading layer and a defective reflectance layer. An accelerated optimization solution is proposed to solve the minimization problem of the intrinsic image decomposition model. Last, the enhanced defect image is obtained by filtering the reflectance image and is then utilized to accurately segment the defect region from the coarse defect map. The experiments conducted using four real-world defect datasets demonstrate that the proposed method outperforms state-of-the-art methods.

In the field of surface defect detection, there is a significant imbalance between the number of positive and negative samples, which has led to a growing interest in positive-samples-based anomaly detection methods. Reconstruction-based methods are currently the most commonly used approach, but they often struggle to repair abnormal foregrounds and reconstruct clear backgrounds simultaneously. To address this issue, we propose a new approach called the forgetting-inputting-based feature fusion and multiple hierarchical feature difference network (FIM-Net). The FIM-Net method incorporates a novel latent feature repair module (LFRM), which combines encoding and memory-encoding obtained by memory-augmented module (MAM) via a novel forgetting-inputting-based feature fusion module (FIFFM) to repair abnormal foregrounds while preserving clear backgrounds. Additionally, we introduce a manual defect generation algorithm (MDGA) to simulate realistic and feature-rich anomalies. Finally, we use a multiple hierarchical feature difference (MHFD) for defect segmentation to achieve more accurate defect location. Our extensive comparison experiments demonstrate that the FIM-Net method achieves state-of-the-art detection accuracy and shows great potential for industrial applications.