计算机工程2025，Vol.51Issue(4)：75-84,10.DOI:10.19678/j.issn.1000-3428.0069704
基于无源领域自适应的低光照显著性目标检测
Low-Light Salient Object Detection Based on Source-Free Domain Adaptation
摘要
Abstract
To address the security issues arising from the degradation of image quality and monitor the effectiveness of surveillance cameras in low-light campus environments,a low-light Salient Object Detection(SOD)method is proposed to enhance target detection capability under low-light conditions.Given the challenges of weakened salient features and the lack of large-scale annotated data in low-light images,a Source-Free Domain Adaptation(SFDA)method for low-light SOD is proposed to transfer the model knowledge trained on normal-lighting images(source domain)to low-light images(target domain).The proposed method employs a two-stage strategy.In the first stage,pseudo-labels for low-light images are generated using the source domain model.To improve the quality of the pseudo-label generation,an ensemble entropy minimization loss is proposed to suppress high-entropy regions.In addition,a selective voting method is introduced to enhance pseudo-label generation.In the second stage,a teacher-student network self-training method based on enhanced guided consistency is employed to refine the saliency maps,further improving the accuracy of the detection results.Experimental results on the SOD-LL dataset show that the proposed method outperforms other image saliency detection methods in low-light scenarios.Compared to normal-light SOD methods,the Mean Absolute Error(MAE)is reduced by 15.15％,and the Weighted F1 value(wFm)is increased by 4.73％.关键词
显著性目标检测/低光照场景/无源领域自适应/伪标签/教师-学生网络/集合熵最小化/选择性投票Key words
Salient Object Detection(SOD)/low-light scenes/Source-Free Domain Adaption(SFDA)/pseudo-label/teacher-student network/ensemble entropy minimization/selective voting分类
计算机与自动化引用本文复制引用
李书玮,黄正翔,胡云,刘兴,卢笑,郭畅,吴成中,王耀南..基于无源领域自适应的低光照显著性目标检测[J].计算机工程,2025,51(4):75-84,10.基金项目
国家自然科学基金(62007007,62277004) （62007007,62277004）
湖南省学位与研究生教学改革研究重点项目(2022JGZD026) （2022JGZD026）
湖南省自然科学基金(2023JJ30415,2022JJ30395) （2023JJ30415,2022JJ30395）
江西省重大科技研发专项项目(20232ACC01007,20232ABC03A09) （20232ACC01007,20232ABC03A09）
吉安市科技计划"揭榜挂帅"项目(20233TGV06020). （20233TGV06020）
