Self-verificated UAED model for edge detection

This study enhances the UAED (Universal Adversarial Edge Detection) model to improve edge detection accuracy. We introduce cross-validated adaptive weights and a “majority voting” mechanism for historical predictions to optimize the model’s performance. The BSDS dataset is preprocessed by resizing images to $320 \times 480$ pixels and splitting them into $160 \times 240$ pixel sub-images. The “majority voting” mechanism generates more reliable labels, reducing noise. We also enhance the model by generating a mask to evaluate pixel edge reliability and dynamically adjusting loss weights based on edge prediction consistency. A key contribution is our alternating loss function strategy, which serves as a regularizer, preventing overfitting and improving generalization. Results show significant improvements in OIS and ODS scores, and a slight improvement in AP scores, validating the effectiveness of our method.