Photometric stereo multi-information fusion unsupervised anomaly detection algorithm

Abstract

Due to different materials, product surfaces are susceptible to light, shadow, reflection, and other factors. Coupled with the appearance of defects of various shapes and types, as well as dust, impurities, and other interfering influences, normal and abnormal samples are difficult to distinguish and a common problem in the field of defect detection. Given this, this paper proposes an end-to-end photometric stereo multi-information fusion unsupervised anomaly detection model. First, the photometric stereo feature generator is used to obtain normal, reflectance, depth, and other information to reconstruct the 3D topographic details of the object’s surface. Second, a multi-scale channel attention mechanism is constructed to fully use the feature associations of different layers of the backbone network, and the limited feature information is used to enhance the defect characterization ability. Finally, the original image is fused with normal and depth features to find the feature variability between defects and defects, as well as between defects and background. The feature differences between the source and clone networks are utilized to achieve multi-scale detection and improve detection accuracy. In this paper, the model performance is verified on the PSAD dataset. The experimental results show that the algorithm in this paper has higher detection accuracy compared with other algorithms. Among them, the multi-scale attention mechanism and multi-information fusion input improve the detection accuracy by 2.56% and 1.57%, respectively. In addition, the ablation experiments further validate the effectiveness of the detection algorithm in this paper.