Year: 2025
Type: article
Abstract: Due to the many difficulties in accurately locating edges or boundaries in images of animals, plants, buildings, and the like with complex backgrounds, edge detection has become one of the most challenging tasks in the field of computer vision and is also a key step in many computer vision applications. Although existing deep learning-based methods can detect the edges of images relatively well, when the image background is rather complex and the key target is small, accurately detecting the edge of the main body and removing background interference remains a daunting task. Therefore, this paper proposes a multi-scale edge detection network based on spatial-frequency domain interactive attention, aiming to achieve accurate detection of the edge of the main target on multiple scales. The use of the spatial-frequency domain interactive attention module can not only perform significant edge extraction by filtering out some interference in the frequency domain. Moreover, by utilizing the interaction between the frequency domain and the spatial domain, edge features at different scales can be extracted and analyzed more accurately. The obtained results are superior to the current edge detection networks in terms of performance indicators and output image quality.
Source: Frontiers in Neurorobotics
Authors: Yongfei Guo, Bo Li, Wenyue Zhang, Weilong Dong
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4409866450