融合注意力机制的多尺度显著性目标检测网络

doi:10.19665/j.issn1001-2400.2022.04.014

Abstract

Abstract:

At present,most salient object detection algorithms are disturbed by the complex background of the image,and the detection results show the phenomena of uneven brightness and blurred edges.To address the above issues,a salient object detection network combining attention mechanism and multi-scale feature fusion is proposed.First,the network is based on the encoder-decoder architecture and the features from adjacent layers are connected in the encoding and decoding process,which captures the multi-scale salient objects in the image.Second,the attention mechanism is integrated in the network to focus on the spatial information and channel information of features,with the purpose of obtaining uniform and complete salient object detection results with clear edges.Finally,a parallel multi-branch structure,named Context Feature Extraction Module,is used to extract features under different receptive fields to improve the performance of salient object detection.Experimental results show that the proposed method can not only accurately locate and highlight the salient objects,but also accurately predict the edge of the salient object in the complex background.Compared with the contrast methods,the average absolute error of MAE and F-Measure on the salient object detection dataset ECSSD can be improved by at least 10% and 0.7%,respectively.

Key words: salient object detection, attention mechanism, multi-scale feature fusion, deep learning, image processing

CLC Number:

TP391.4

LIU Di,GUO Jichang,WANG Yudong,ZHANG Yi. Multi-scale salient object detection network combining an attention mechanism[J].Journal of Xidian University, 2022, 49(4): 118-126.

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References 24

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评价指标	U-Net	DSS	PicaNet	PoolNet	GCPANet	AMNet
MAE	0.095	0.088	0.097	0.052	0.049	0.044
F-measure	0.793	0.839	0.811	0.893	0.901	0.908

评价指标	Baseline	Baseline +CASA	Baseline +CFEM	AMNet
MAE	0.095	0.082	0.075	0.044
F-measure	0.793	0.848	0.849	0.908

Multi-scale salient object detection network combining an attention mechanism

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