A Salient Object Detection Method Based on Boundary Enhancement
Author:
Wen Falin1, Wang Qinghui1, Zou Ruirui1, Wang Ying1, Liu Fenglin1, Chen Yang1ORCID, Yu Linghao2, Du Shaoyi3, Yuan Chengzhi4
Affiliation:
1. School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan 364012, China 2. School of Software Engineering, Xi’an Jiaotong University, Xi’an 710049, China 3. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an 710049, China 4. Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881, USA
Abstract
Visual saliency refers to the human’s ability to quickly focus on important parts of their visual field, which is a crucial aspect of image processing, particularly in fields like medical imaging and robotics. Understanding and simulating this mechanism is crucial for solving complex visual problems. In this paper, we propose a salient object detection method based on boundary enhancement, which is applicable to both 2D and 3D sensors data. To address the problem of large-scale variation of salient objects, our method introduces a multi-level feature aggregation module that enhances the expressive ability of fixed-resolution features by utilizing adjacent features to complement each other. Additionally, we propose a multi-scale information extraction module to capture local contextual information at different scales for back-propagated level-by-level features, which allows for better measurement of the composition of the feature map after back-fusion. To tackle the low confidence issue of boundary pixels, we also introduce a boundary extraction module to extract the boundary information of salient regions. This information is then fused with salient target information to further refine the saliency prediction results. During the training process, our method uses a mixed loss function to constrain the model training from two levels: pixels and images. The experimental results demonstrate that our salient target detection method based on boundary enhancement shows good detection effects on targets of different scales, multi-targets, linear targets, and targets in complex scenes. We compare our method with the best method in four conventional datasets and achieve an average improvement of 6.2% on the mean absolute error (MAE) indicators. Overall, our approach shows promise for improving the accuracy and efficiency of salient object detection in a variety of settings, including those involving 2D/3D semantic analysis and reconstruction/inpainting of image/video/point cloud data.
Funder
Natural Science Basic Research Program of Shaanxi Natural Science Foundation of Fujian Province
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference32 articles.
1. Zhang, P., Wang, D., Lu, H., Wang, H., and Ruan, X. (2017, January 22–29). Amulet: Aggregating multi-level convolutional features for salient object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy. 2. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs;Chen;IEEE Trans. Pattern Anal. Mach. Intell.,2017 3. Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21–26). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA. 4. Deep saliency detection via channel-wise hierarchical feature responses;Li;Neurocomputing,2018 5. Liu, N., Han, J., and Yang, M.H. (2018, January 18–23). Picanet: Learning pixel-wise contextual attention for saliency detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.
|
|