Innovating Edge Detection with Self-Attention Mechanisms and Novel Post-Processing Modules

In previous studies on edge extraction, researchers have consistently sought to balance efficiency, quality, and stability. To address this, we propose an edge detection algorithm that leverages the self-attention mechanism and post-processing methods to enhance the quality and stability of edge extraction in image processing. In recent years, self-attention mechanisms have emerged as a core component in many cutting-edge models, particularly in the fields of Natural Language Processing (NLP) and Computer Vision. They are capable of capturing long-range dependencies within the data. Consequently, we introduce the self-attention mechanism to enhance the capability of feature representation. By employing channel compression and scaling factors to stabilize the computation of attention scores, we ensure the reliability of the training process. Next, we combine Gaussian blurring with adaptive thresholding in the post-processing stage to reduce noise and detail interference, thereby improving edge detection accuracy. Experimental results on extensive benchmark datasets demonstrate that the proposed algorithm significantly outperforms existing methods in terms of edge detection accuracy and stability, particularly in the presence of complex backgrounds and noise. Additionally, the method exhibits strong computational efficiency, making it suitable for practical application. These advancements offer new insights and approaches for the development of edge detection technology.