{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T02:52:03Z","timestamp":1747191123281,"version":"3.40.5"},"reference-count":41,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,7,20]],"date-time":"2022-07-20T00:00:00Z","timestamp":1658275200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Sensors"],"published-print":{"date-parts":[[2022,7,20]]},"abstract":"<jats:p>With the recent development of deep convolutional neural network (CNN), remote sensing for ship detection methods has achieved enormous progress. However, current methods focus on the whole ships and fail on the component\u2019s detection of a ship. To detect ships from remote-sensing images in a more refined way, we employ the inherent relationship between ships and their critical parts to establish a multilevel structure and propose a novel framework to improve the performance in identifying the multilevel objects. Our framework, named the dual detector network (DD-Net), consists of two carefully designed detectors, one for ships (the ship detector) and the other for their critical parts (the critical part detector), for detecting the critical parts in a coarse-to-fine manner. The ship detector offers detection results of the ship, based on which the critical part detector detects small critical parts inside each ship region. The framework is trained in an end-to-end way by optimizing the multitask loss. Due to the lack of publicly available datasets for critical part detection, we build a new dataset named RS-Ship with 1015 remote-sensing images and 2856 annotations. Experiments on the HRSC2016 dataset and the RS-Ship dataset show that our method performs well in the detection of ships and critical parts.<\/jats:p>","DOI":"10.1155\/2022\/9602100","type":"journal-article","created":{"date-parts":[[2022,7,20]],"date-time":"2022-07-20T22:50:09Z","timestamp":1658357409000},"page":"1-12","source":"Crossref","is-referenced-by-count":0,"title":["DD-Net: A Dual Detector Network for Multilevel Object Detection in Remote-Sensing Images"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3817-5706","authenticated-orcid":true,"given":"Dongdong","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Electronic and Optical Engineering, People Liberation Army Engineering University, Shijiazhuang Hebei 050003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3841-1919","authenticated-orcid":true,"given":"Chunping","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electronic and Optical Engineering, People Liberation Army Engineering University, Shijiazhuang Hebei 050003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3831-9856","authenticated-orcid":true,"given":"Qiang","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Electronic and Optical Engineering, People Liberation Army Engineering University, Shijiazhuang Hebei 050003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.3390\/rs13020281"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.3390\/e22020249"},{"key":"3","first-page":"240","article-title":"A sea-land segmentation algorithm based on graph theory","volume":"9901","author":"Z. Huang","year":"2016","journal-title":"2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015)."},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1109\/ISIDF.2011.6024201"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1109\/EORSA.2016.7552845"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2939201"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2018.2813094"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-22808-8_39"},{"first-page":"2961","article-title":"Mask r-Cnn","author":"K. He","key":"9"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2929257"},{"first-page":"684","article-title":"Pose Partition Networks for Multi-Person Pose Estimation","author":"X. Nie","key":"11"},{"first-page":"437","article-title":"MultiPoseNet: fast multi-person pose estimation using pose residual network","author":"M. Kocabas","key":"12"},{"key":"13","doi-asserted-by":"crossref","first-page":"324","DOI":"10.5220\/0006120603240331","article-title":"A high resolution optical satellite image dataset for ship recognition and some new baselines","volume":"2","author":"Z. Liu","year":"2017","journal-title":"International Conference on Pattern Recognition Applications and Methods"},{"first-page":"11969","article-title":"PifPaf: composite fields for human pose estimation","author":"S. Kreiss","key":"14"},{"first-page":"580","article-title":"Rich feature hierarchies for accurate object detection and semantic segmentation","author":"R. Girshick","key":"15"},{"first-page":"1440","article-title":"Fast R-CNN","author":"R. Girshick","key":"16"},{"key":"17","first-page":"91","article-title":"Faster R-CNN: towards real-time object detection with region proposal networks","volume":"28","author":"S. Ren","year":"2015","journal-title":"Advances in Neural Information Processing Systems"},{"article-title":"DSSD: deconvolutional single shot detector","year":"2017","author":"C. Y. Fu","key":"18"},{"first-page":"779","article-title":"You only look once: unified, real-time object detection","author":"J. Redmon","key":"19"},{"first-page":"7263","article-title":"YOLO9000: better, faster, stronger","author":"J. Redmon","key":"20"},{"article-title":"Yolov3: an incremental improvement","year":"2018","author":"J. Redmon","key":"21"},{"issue":"5","key":"22","first-page":"102","article-title":"A ship detection method for remote-sensing images based on improved YOLO-v3","volume":"27","author":"M. Gong","year":"2020","journal-title":"Electronics Optics & Control"},{"first-page":"1","article-title":"Going deeper with convolutions","author":"C. Szegedy","key":"23"},{"first-page":"693","article-title":"Inshore ship detection based on Mask R-CNN","author":"S. Nie","key":"24"},{"first-page":"900","article-title":"Rotated region based CNN for ship detection","author":"Z. Liu","key":"25"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3095186"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.3390\/rs12071196"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2019.12.001"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1080\/2150704X.2021.1895445"},{"first-page":"740","article-title":"Microsoft coco: common objects in context","author":"T. Y. Lin","key":"30"},{"first-page":"7773","article-title":"Posefix: model-agnostic general human pose refinement network","author":"G. Moon","key":"31"},{"first-page":"10863","article-title":"Crowdpose: efficient crowded scenes pose estimation and a new benchmark","author":"J. Li","key":"32"},{"first-page":"525","article-title":"Deep dual consecutive network for human pose estimation","author":"Z. Liu","key":"33"},{"article-title":"Yolov4: optimal speed and accuracy of object detection","year":"2020","author":"A. Bochkovskiy","key":"34"},{"first-page":"248","article-title":"ImageNet: a large-scale hierarchical image database","author":"J. Deng","key":"35"},{"first-page":"936","article-title":"Feature pyramid networks for object detection","author":"T. Y. Lin","key":"36"},{"key":"37","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2021.3102137"},{"key":"38","article-title":"Object detection in remote sensing images based on deep transfer learning","volume":"1-17","author":"J. Chen","year":"2021","journal-title":"Multimedia Tools and Applications"},{"first-page":"2999","article-title":"Focal loss for dense object detection","author":"T. Y. Lin","key":"39"},{"first-page":"13034","article-title":"You only look one-level feature","author":"Q. Chen","key":"40"},{"first-page":"3490","article-title":"TOOD: task-aligned one-stage object detection","author":"C. Feng","key":"41"}],"container-title":["Journal of Sensors"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/js\/2022\/9602100.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/js\/2022\/9602100.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/js\/2022\/9602100.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,20]],"date-time":"2022-07-20T22:50:14Z","timestamp":1658357414000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/js\/2022\/9602100\/"}},"subtitle":[],"editor":[{"given":"Yuxing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2022,7,20]]},"references-count":41,"alternative-id":["9602100","9602100"],"URL":"https:\/\/doi.org\/10.1155\/2022\/9602100","relation":{},"ISSN":["1687-7268","1687-725X"],"issn-type":[{"type":"electronic","value":"1687-7268"},{"type":"print","value":"1687-725X"}],"subject":[],"published":{"date-parts":[[2022,7,20]]}}}