Salient Object Detection Based on Multi-Strategy Feature Optimization  

作　　者：Libo Han Sha Tao Wen Xia Weixin Sun Li Yan Wanlin Gao 

机构地区：[1]College of Information and Electrical Engineering,China Agricultural University,Beijing,100083,China [2]Key Laboratory of Agricultural Informatization Standardization,Ministry of Agriculture and Rural Affairs,China Agricultural University,Beijing,100083,China [3]Pu’er University,Pu’er,665000,China

出　　处：《Computers, Materials & Continua》2025年第2期2431-2449,共19页计算机、材料和连续体(英文)

摘　　要：At present, salient object detection (SOD) has achieved considerable progress. However, the methods that perform well still face the issue of inadequate detection accuracy. For example, sometimes there are problems of missed and false detections. Effectively optimizing features to capture key information and better integrating different levels of features to enhance their complementarity are two significant challenges in the domain of SOD. In response to these challenges, this study proposes a novel SOD method based on multi-strategy feature optimization. We propose the multi-size feature extraction module (MSFEM), which uses the attention mechanism, the multi-level feature fusion, and the residual block to obtain finer features. This module provides robust support for the subsequent accurate detection of the salient object. In addition, we use two rounds of feature fusion and the feedback mechanism to optimize the features obtained by the MSFEM to improve detection accuracy. The first round of feature fusion is applied to integrate the features extracted by the MSFEM to obtain more refined features. Subsequently, the feedback mechanism and the second round of feature fusion are applied to refine the features, thereby providing a stronger foundation for accurately detecting salient objects. To improve the fusion effect, we propose the feature enhancement module (FEM) and the feature optimization module (FOM). The FEM integrates the upper and lower features with the optimized features obtained by the FOM to enhance feature complementarity. The FOM uses different receptive fields, the attention mechanism, and the residual block to more effectively capture key information. Experimental results demonstrate that our method outperforms 10 state-of-the-art SOD methods.

关 键 词：Salient object detection multi-strategy feature optimization feedback mechanism 

分 类 号：G63[文化科学—教育学]