Guo Yongfei, Li Bo, Zhang Wenyue, Dong Weilong
Xi'an Jieda Measurement & Control Co., Ltd., Xi'an, China.
Front Neurorobot. 2025 Apr 28;19:1550939. doi: 10.3389/fnbot.2025.1550939. eCollection 2025.
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.
由于在具有复杂背景的动物、植物、建筑物等图像中准确定位边缘或边界存在诸多困难，边缘检测已成为计算机视觉领域最具挑战性的任务之一，也是许多计算机视觉应用中的关键步骤。尽管现有的基于深度学习的方法能够较好地检测图像边缘，但当图像背景相当复杂且关键目标较小时，准确检测主体边缘并去除背景干扰仍然是一项艰巨的任务。因此，本文提出了一种基于空间 - 频域交互注意力的多尺度边缘检测网络，旨在实现对主目标边缘在多个尺度上的准确检测。使用空间 - 频域交互注意力模块不仅可以通过在频域中滤除一些干扰来进行显著的边缘提取。此外，通过利用频域和空间域之间的交互，可以更准确地提取和分析不同尺度的边缘特征。在性能指标和输出图像质量方面，所获得的结果优于当前的边缘检测网络。
