{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/masnet-a-robust-deep-marine-animal","title":"MASNet: A Robust Deep Marine Animal Segmentation Network","arxiv_id":null,"date":"2023-05-01","proceeding":"IEEE Journal of Oceanic Engineering 2023 5","authors":["Zhenqi Fu","Ruizhe Chen","Yue Huang","En Cheng","Xinghao Ding","Kai-Kuang Ma"],"abstract":"Marine animal studies are of great importance to human beings and instrumental to many research areas. How to identify such animals through image processing is a challenging task that leads to marine animal segmentation (MAS). Although deep neural networks have been widely applied for object segmentation, few of them consider the complex imaging condition in the water and the camouflage property of marine animals. To this end, a robust deep marine animal segmentation network is proposed in this article. Specifically, we design a new data augmentation strategy to randomly change the degradation and camouflage attributes of the original objects. With the augmentations, a fusion-based deep neural network constructed in a Siamese manner is trained to learn the shared semantic representations. Moreover, we construct a new large-scale real-world MAS data set for conducting extensive experiments. It consists of over 3000 images with various underwater scenes and objects. Each image is annotated with an object-level mask and assigned to a category. Extensive experimental results show that our method significantly outperforms 12 state-of-the-art methods both qualitatively and quantitatively.","url_abs":"https://ieeexplore.ieee.org/document/10113781","url_pdf":"https://ieeexplore.ieee.org/document/10113781","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"masnet-a-robust-deep-marine-animal","repo_url":"https://github.com/zhenqifu/MASNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"marine-animal-segmentation","task_name":"Marine Animal Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-segmentation-on-mas3k","task":"Image Segmentation","dataset":"MAS3K","model":"MASNet","rank_in_archive_order":3,"of":4,"metrics":{"E-measure":"0.906","MAE":"0.032","S-measure":"0.864","mIoU":"0.742"},"uses_additional_data":false},{"leaderboard":"/sota/image-segmentation-on-rmas","task":"Image Segmentation","dataset":"RMAS","model":"MASNet","rank_in_archive_order":3,"of":4,"metrics":{"E-measure":"0.920","MAE":"0.024","S-measure":"0.862","mIoU":"0.731"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}