Anomaly Segmentation Based on Cosine Similarity Edge Iteration Smoothing
Yongheng Ren, Cong Wang, Changzhen Xiong · 2023
At present, the existing anomaly segmentation algorithm directly uses the abnormal score of the trained network output, such as the cosine similarity score. However, due to the uncertainty of the edge, the edge pixels or pixels near the edge are less similar to the class prototype. It shows more false negatives at the target edge during inferring, which will greatly affect the performance of the anomalous segmentation. This paper proposed to solve the problem of more false negatives in target edges. First, we need to calculate the cosine similarity between the input pixel and the class prototype. The cosine similarity calculation image was performed using morphological edge detection to extract the false negative edges, features with high non-edge similarity were screened by the edge-aware pooling layer, the filtered features are used to update the edges part. Finally, the updated cosine similarity calculation image is treated with expansion smoothing. The experiments presented were conducted in the StreetHazards Dataset, the FPR95 was 19.6%, AUROC rate was 92.5%, AUPR was 12.5%. The method in this paper is simple and the network does not require retraining, and the precision is close to SOTA.