摘要
显著性目标检测旨在准确检测和定位图像或视频中最引人注目的目标或区域,为更好地进行目标识别和场景分析提供帮助。尽管全监督显著性检测方法取得一定成效,但获取大规模像素级标注数据集十分困难且昂贵。弱监督检测方法利用相对容易获取的图像级标签或带噪声的弱标签训练模型,在实际应用中表现出良好效果。全面对比了全监督和弱监督显著性检测的主流方法和应用场景,重点分析了常用的弱标签数据标注方法及其对显著目标检测的影响。综述了弱监督条件下显著目标检测方法的最新研究进展,并在常用数据集上对不同弱监督方法的性能进行了比较。最后探讨了弱监督显著性检测在农业、医学和军事等特殊领域的应用前景,指出了该研究领域存在的问题及未来发展趋势。
Salient object detection aims to accurately detect and locate the most attention-grabbing objects or regions in images or videos,facilitating better object recognition and scene analysis.Despite the effectiveness of fully supervised saliency detection methods,acquiring large pixel-level annotated datasets is challenging and costly.Weakly supervised detection methods utilize relatively easy-to-obtain image-level labels or noisy weak labels to train models,demonstrating good performance in practical applications.This paper comprehensively compares the mainstream methods and applica-tion scenarios of fully supervised and weakly supervised saliency detection methods,and then analyzes the data annota-tion methods using weak labels and their impact on salient object detection.The latest research progress in salient object detection under weakly supervised conditions is reviewed,and the performance of various weakly supervised methods is compared on several public datasets.Finally,the potential applications of weakly supervised saliency detection methods in special fields such as agriculture,medicine and military are discussed,highlighting the existing challenges and future development trends in this research area.
作者
于俊伟
郭园森
张自豪
母亚双
YU Junwei;GUO Yuansen;ZHANG Zihao;MU Yashuang(Key Laboratory of Grain Information Processing and Control of the Ministry of Education(Henan University of Tech-nology),Zhengzhou 450001,China;College of Artificial Intelligence and Big Data,Henan University of Technology,Zhengzhou 450001,China;College of Information Science and Engineering,Henan University of Technology,Zhengzhou 450001,China)
出处
《计算机工程与应用》
CSCD
北大核心
2024年第10期1-15,共15页
Computer Engineering and Applications
基金
国家自然科学基金青年基金(62006071)
2021年度河南省重点研发与推广专项(212102210152)。
关键词
显著性目标检测
全监督学习
弱监督学习
salient object detection
fully supervised learning
weakly supervised learning
