| Title | Salient Object Detection Based on Multi-layer Cascade and Fine Boundary |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Sun, Dengdi, Lv, Xiangjie, Huang, Shilei, Yao, Lin, Ding, Zhuanlian |
| Conference Name | 2021 17th International Conference on Computational Intelligence and Security (CIS) |
| Date Published | nov |
| Keywords | composability, Computational modeling, Cross Layer Security, data mining, Deep Learning, feature extraction, Feature fusion, hybrid loss, object detection, pubcrawl, Resiliency, saliency detection, security, Semantics |
| Abstract | Due to the continuous improvement of deep learning, saliency object detection based on deep learning has been a hot topic in computational vision. The Fully Convolutional Neural Network (FCNS) has become the mainstream method in salient target measurement. In this article, we propose a new end-to-end multi-level feature fusion module(MCFB), success-fully achieving the goal of extracting rich multi-scale global information by integrating semantic and detailed information. In our module, we obtain different levels of feature maps through convolution, and then cascade the different levels of feature maps, fully considering our global information, and get a rough saliency image. We also propose an optimization module upon our base module to further optimize the feature map. To obtain a clearer boundary, we use a self-defined loss function to optimize the learning process, which includes the Intersection-over-Union (IoU) losses, Binary Cross-Entropy (BCE), and Structural Similarity (SSIM). The module can extract global information to a greater extent while obtaining clearer boundaries. Compared with some existing representative methods, this method has achieved good results. |
| DOI | 10.1109/CIS54983.2021.00069 |
| Citation Key | sun_salient_2021 |
Salient Object Detection Based on Multi-layer Cascade and Fine Boundary