Work

Weak Edge Target Segmentation Network Based on Dual Attention Mechanism

Year: 2024

Type: article

Abstract: Segmentation of weak edge targets such as glass and plastic poses a challenge in the field of target segmentation. The detection process is susceptible to background interference and various external factors due to the transparent nature of these materials. To address this issue, this paper introduces a segmentation network for weak edge target objects (WETS-Net). To effectively extract edge information of such objects and eliminate redundant information during feature extraction, a dual-attention mechanism is employed, including the Edge Attention Extraction Module (EAEM) and the Multi-Scale Information Fusion Module (MIFM). Specifically, the EAEM combines improved edge feature extraction kernels to selectively enhance the importance of edge features, aiding in more precise target region extraction. The MIFM utilizes spatial attention mechanisms to fuse multi-scale features, reducing background and external interference. These innovations enhance the performance of WETS-Net, offering a new direction for weak edge target segmentation research. Finally, through ablation experiments, the effectiveness of each module is effectively validated. Moreover, the proposed algorithm achieves an average detection accuracy of 95.83% and 96.13% on the dataset and a self-made dataset, respectively, outperforming similar U-Net-improved networks.

Source: Applied Sciences

DOI: 10.3390/app14198963

Authors: Nengkai Wu, Dongyao Jia, Ziqi Li, Zihao He

Institution: Beijing Jiaotong University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,728

Funder: Beijing Jiaotong University