{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T13:21:54Z","timestamp":1777555314990,"version":"3.51.4"},"reference-count":51,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T00:00:00Z","timestamp":1671148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China (NSFC)","award":["6197612"],"award-info":[{"award-number":["6197612"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["62072213"],"award-info":[{"award-number":["62072213"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["ZR2020ZD44"],"award-info":[{"award-number":["ZR2020ZD44"]}]},{"name":"Taishan Young Scholars Program of Shandong Province","award":["6197612"],"award-info":[{"award-number":["6197612"]}]},{"name":"Taishan Young Scholars Program of Shandong Province","award":["62072213"],"award-info":[{"award-number":["62072213"]}]},{"name":"Taishan Young Scholars Program of Shandong Province","award":["ZR2020ZD44"],"award-info":[{"award-number":["ZR2020ZD44"]}]},{"name":"Key Development Program for Basic Research of Shandong Province","award":["6197612"],"award-info":[{"award-number":["6197612"]}]},{"name":"Key Development Program for Basic Research of Shandong Province","award":["62072213"],"award-info":[{"award-number":["62072213"]}]},{"name":"Key Development Program for Basic Research of Shandong Province","award":["ZR2020ZD44"],"award-info":[{"award-number":["ZR2020ZD44"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Saliency detection is a key research topic in the field of computer vision. Humans can be accurately and quickly mesmerized by an area of interest in complex and changing scenes through the visual perception area of the brain. Although existing saliency-detection methods can achieve competent performance, they have deficiencies such as unclear margins of salient objects and the interference of background information on the saliency map. In this study, to improve the defects during saliency detection, a multiscale cascaded attention network was designed based on ResNet34. Different from the typical U-shaped encoding\u2013decoding architecture, we devised a contextual feature extraction module to enhance the advanced semantic feature extraction. Specifically, a multiscale cascade block (MCB) and a lightweight channel attention (CA) module were added between the encoding and decoding networks for optimization. To address the blur edge issue, which is neglected by many previous approaches, we adopted the edge thinning module to carry out a deeper edge-thinning process on the output layer image. The experimental results illustrate that this method can achieve competitive saliency-detection performance, and the accuracy and recall rate are improved compared with those of other representative methods.<\/jats:p>","DOI":"10.3390\/s22249950","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T09:31:01Z","timestamp":1671442261000},"page":"9950","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Multiscale Cascaded Attention Network for Saliency Detection Based on ResNet"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4249-2264","authenticated-orcid":false,"given":"Muwei","family":"Jian","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China"},{"name":"School of Information Science and Technology, Linyi University, Linyi 276012, China"}]},{"given":"Haodong","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China"}]},{"given":"Xiangyu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China"}]},{"given":"Linsong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"114219","DOI":"10.1016\/j.eswa.2020.114219","article-title":"Visual saliency detection by integrating spatial position prior of object with background cues","volume":"168","author":"Jian","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"33465","DOI":"10.1007\/s11042-019-07842-4","article-title":"Saliency detection using multiple low-level priors and a prop-agation mechanism","volume":"79","author":"Jian","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1007\/s00530-022-00940-8","article-title":"Visual saliency detection via combining center prior and U-Net","volume":"28","author":"Lu","year":"2022","journal-title":"Multimedia Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/34.730558","article-title":"A model of saliency-based visual attention for rapid scene analysis","volume":"20","author":"Itti","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1109\/TPAMI.2006.86","article-title":"A coherent computational approach to model bottom-up visual attention","volume":"28","author":"Barba","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mathe, S., and Sminchisescu, C. (2012, January 7\u201313). Dynamic Eye Movement Datasets and Learnt Saliency Models for Visual Action Recognition. Proceedings of the European Conference on Computer Vision, Florence, Italy.","DOI":"10.1007\/978-3-642-33709-3_60"},{"key":"ref_7","first-page":"1923","article-title":"Action from still image dataset and inverse optimal control to learn task specific visual scanpaths","volume":"26","author":"Mathe","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1109\/TPAMI.2014.2366154","article-title":"Actions in the Eye: Dynamic Gaze Datasets and Learnt Saliency Models for Visual Recognition","volume":"37","author":"Mathe","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1167\/8.7.32","article-title":"SUN: A Bayesian framework for saliency using natural statistics","volume":"8","author":"Zhang","year":"2008","journal-title":"J. Vis."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hou, X., and Zhang, L. (2007, January 17\u201322). Saliency Detection: A Spectral Residual Approach. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383267"},{"key":"ref_11","first-page":"155","article-title":"Saliency based on information maximization","volume":"18","author":"Bruce","year":"2005","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_12","first-page":"545","article-title":"Graph-based visual saliency","volume":"19","author":"Harel","year":"2006","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_13","first-page":"481","article-title":"Discriminant saliency for visual recognition from cluttered scenes","volume":"17","author":"Gao","year":"2004","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_14","unstructured":"Judd, T., Ehinger, K., Durand, F., and Torralba, A. (October, January 29). Learning to Predict Where Humans Look. Proceedings of the IEEE International Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ren, G., Yu, Y., Liu, H., and Stathaki, T. (2022). Dynamic Knowledge Distillation with Noise Elimination for RGB-D Salient Object Detection. Sensors, 22.","DOI":"10.2139\/ssrn.4125204"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Duan, F., Wu, Y., Guan, H., and Wu, C. (2022). Saliency Detection of Light Field Images by Fusing Focus Degree and GrabCut. Sensors, 22.","DOI":"10.3390\/s22197411"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yang, J., Wang, L., and Li, Y. (2022). Feature Refine Network for Salient Object Detection. Sensors, 22.","DOI":"10.3390\/s22124490"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Achanta, R., Estrada, F., Wils, P., and S\u00fcsstrunk, S. (2008). Salient Region Detection and Segmentation. International Conference on Computer Vision Systems, Springer.","DOI":"10.1007\/978-3-540-79547-6_7"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1109\/TPAMI.2014.2345401","article-title":"Global contrast based salient region detection","volume":"37","author":"Cheng","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1109\/TPAMI.2011.272","article-title":"Context-aware saliency detection","volume":"34","author":"Goferman","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Aiello, W., Chung, F., and Lu, L. (2000, January 21\u201323). A Random Graph Model for Massive Graphs. Proceedings of the Thirty-Second Annual ACM Symposium on Theory of Computing, Portland, OR, USA.","DOI":"10.1145\/335305.335326"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1109\/TCYB.2014.2356200","article-title":"Visual-Patch-Attention-Aware Saliency Detection","volume":"45","author":"Jian","year":"2014","journal-title":"IEEE Trans. Cybern."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jvcir.2018.10.008","article-title":"Saliency detection based on directional patches extraction and principal local color contrast","volume":"57","author":"Jian","year":"2018","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_24","unstructured":"Guo, C., Ma, Q., and Zhang, L. (2008, January 23\u201328). Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Achanta, R., and Susstrunk, S. (2010, January 26\u201329). Saliency Detection Using Maximum Symmetric Surround. Proceedings of the IEEE International Conference on Image Processing, Hong Kong, China.","DOI":"10.1109\/ICIP.2010.5652636"},{"key":"ref_26","unstructured":"Hecht-Nielsen, R. (1992). Theory of the Backpropagation Neural Network. Neural Networks for Perception, Academic Press."},{"key":"ref_27","first-page":"3974","article-title":"Deblurring Dynamic Scenes via Spatially Varying Recurrent Neural Networks","volume":"44","author":"Ren","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1007\/s11263-019-01235-8","article-title":"Single Image Dehazing via Multi-scale Convolutional Neural Networks with Holistic Edges","volume":"128","author":"Ren","year":"2016","journal-title":"Int. J. Comput. Vis."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4842","DOI":"10.1109\/TIP.2022.3187565","article-title":"Learning Semantic-Aware Local Features for Long Term Visual Localization","volume":"31","author":"Fan","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Fan, B., Yang, Y., Feng, W., Wu, F., Lu, J., and Liu, H. (2022). Seeing through Darkness: Visual Localization at Night via Weakly Supervised Learning of Domain Invariant Features. IEEE Trans. Multimedia, 1.","DOI":"10.1109\/TMM.2022.3154165"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Luo, A., Li, X., Yang, F., Jiao, Z., Cheng, H., and Lyu, S. (2020, January 8\u201314). Cascade Graph Neural Networks for RGB-D Salient Object Detection. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58610-2_21"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Feng, M., Lu, H., and Ding, E. (2019, January 15\u201320). Attentive Feedback Network for Boundary-Aware Salient Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00172"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, J.J., Hou, Q., Cheng, M.M., Feng, J., and Jiang, J. (2019, January 15\u201320). A simple Pooling-Based Design for Realtime Salient Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00404"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2368","DOI":"10.1109\/TIP.2017.2787612","article-title":"Deep visual attention prediction","volume":"27","author":"Wang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5142","DOI":"10.1109\/TIP.2018.2851672","article-title":"Predicting Human Eye Fixations via an LSTM-Based Saliency Attentive Model","volume":"27","author":"Cornia","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5052","DOI":"10.1109\/TIP.2019.2909649","article-title":"Deep Group-Wise Fully Convolutional Network for Co-Saliency Detection with Graph Propagation","volume":"28","author":"Wei","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhu, D., Dai, L., Luo, Y., Zhang, G., Shao, X., Itti, L., and Lu, J. (2018, January 15\u201318). MAFL: Multi-Scale Adversarial Feature Learning for Saliency Detection. Proceedings of the 2018 International Conference on Control and Computer Vision, New York, NY, USA.","DOI":"10.1145\/3232651.3232673"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1007\/s11263-015-0822-0","article-title":"SuperCNN: A Superpixelwise Convolutional Neural Networkfor Salient Object Detection","volume":"115","author":"He","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hou, Q., Cheng, M.M., Hu, X., Borji, A., Tu, Z., and Torr, P.H. (2017, January 21\u201326). Deeply Supervised Salient Object Detection with Short Connections. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.563"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hui, S., Guo, Q., Geng, X., and Zhang, C. (2022). Multi-Guidance CNNs for Salient Object Detection. ACM Trans. Multimed. Comput. Commun. Appl., Early Access.","DOI":"10.1145\/3570507"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Liu, N., Zhang, N., Wan, K., Shao, L., and Han, J. (2021, January 10\u201317). Visual Saliency Transformer. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00468"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hussain, T., Anwar, A., Anwar, S., Petersson, L., and Baik, S.W. (2022). Pyramidal Attention for Saliency Detection. arXiv.","DOI":"10.1109\/CVPRW56347.2022.00325"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wang, L., Lu, H., Wang, Y., Feng, M., Wang, D., Yin, B., and Ruan, X. (2017, January 21\u201326). Learning to Detect Salient Objects with Image-Level Supervision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.404"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yan, Q., Xu, L., Shi, J., and Jia, J. (2013, January 23\u201328). Hierarchical Saliency Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, USA.","DOI":"10.1109\/CVPR.2013.153"},{"key":"ref_45","unstructured":"Li, G., and Yu, Y. (2015, January 7\u201312). Visual Saliency Based on Multiscale Deep Features. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_46","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1016\/j.ins.2021.08.069","article-title":"Integrating object proposal with attention networks for video saliency detection","volume":"576","author":"Jian","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Deng, Z., Hu, X., Zhu, L., Xu, X., Qin, J., Han, G., and Heng, P.A. (2018, January 13\u201319). R3net: Recurrent residual refinement network for saliency detection. Proceedings of the 27th International Joint Conference on Artificial Intelligence, Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/95"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wang, T., Zhang, L., Wang, S., Lu, H., Yang, G., Ruan, X., and Borji, A. (2018, January 18\u201322). Detect globally, refine locally: A novel approach to saliency detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00330"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Liu, N., Han, J., and Yang, M.H. (2018, January 18\u201323). 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.","DOI":"10.1109\/CVPR.2018.00326"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Gao, S.-H., Tan, Y.-Q., Cheng, M.-M., Lu, C., Chen, Y., and Yan, S. (2020, January 8\u201314). Highly Efficient Salient Object Detection with 100K Parameters. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58539-6_42"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9950\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:43:06Z","timestamp":1760146986000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9950"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,16]]},"references-count":51,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22249950"],"URL":"https:\/\/doi.org\/10.3390\/s22249950","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,16]]}}}