{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T14:20:01Z","timestamp":1769005201014,"version":"3.49.0"},"reference-count":49,"publisher":"Emerald","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,11]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Based on the strong feature representation ability of the convolutional neural network (CNN), generous object detection methods in remote sensing (RS) have been proposed one after another. However, due to the large variation in scale and the omission of relevant relationships between objects, there are still great challenges for object detection in RS. Most object detection methods fail to take the difficulties of detecting small and medium-sized objects and global context into account. Moreover, inference time and lightness are also major pain points in the field of RS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design\/methodology\/approach<\/jats:title>\n                    <jats:p>To alleviate the aforementioned problems, this study proposes a novel method for object detection in RS, which is called lightweight object detection with a multi-receptive field and long-range dependency in RS images (MFLD). The multi-receptive field extraction (MRFE) and long-range dependency information extraction (LDIE) modules are put forward.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>To concentrate on the variability of objects in RS, MRFE effectively expands the receptive field by a combination of atrous separable convolutions with different dilated rates. Considering the shortcomings of CNN in extracting global information, LDIE is designed to capture the relationships between objects. Extensive experiments over public datasets in RS images demonstrate that our MFLD method surpasses the state-of-the-art methods. Most of all, on the NWPU VHR-10 dataset, our MFLD method achieves 94.6% mean average precision with 4.08\u00a0M model volume.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Originality\/value<\/jats:title>\n                    <jats:p>This paper proposed a method called lightweight object detection with multi-receptive field and long-range dependency in RS images.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1108\/ijicc-01-2024-0020","type":"journal-article","created":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T13:02:43Z","timestamp":1725627763000},"page":"805-823","source":"Crossref","is-referenced-by-count":5,"title":["MFLD: lightweight object detection with multi-receptive field and\u00a0long-range dependency in\u00a0remote sensing images"],"prefix":"10.1108","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4967-6008","authenticated-orcid":false,"given":"Weixing","family":"Wang","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence , , Guangzhou,","place":["China"]},{"name":"The Open University of Guangdong , , Guangzhou,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7788-7023","authenticated-orcid":false,"given":"Yixia","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer and Cyber Security , , Fuzhou,","place":["China"]},{"name":"Fujian Normal University , , Fuzhou,","place":["China"]}]},{"given":"Mingwei","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Computer and Cyber Security , , Fuzhou,","place":["China"]},{"name":"Fujian Normal University , , Fuzhou,","place":["China"]}]}],"member":"140","published-online":{"date-parts":[[2024,9,9]]},"reference":[{"issue":"4","key":"2025102222544386800_ref001","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/tpami.2017.2699184","article-title":"Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2025102222544386800_ref002","first-page":"801","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","author":"Chen","year":"2018"},{"key":"2025102222544386800_ref003","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120519","article-title":"Consistency- and dependence-guided knowledge distillation for object detection in remote sensing images","volume":"229","author":"Chen","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"2025102222544386800_ref004","first-page":"2884","article-title":"Rifd-cnn: rotation-invariant and Fisher discriminative convolutional neural networks for object detection","author":"Cheng","year":"2016"},{"key":"2025102222544386800_ref005","first-page":"379","article-title":"R-fcn: object detection via region-based fully convolutional networks","volume":"29","author":"Dai","year":"2016","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2025102222544386800_ref006","first-page":"12124","article-title":"Cswin transformer: a general vision transformer backbone with cross-shaped windows","author":"Dong","year":"2022"},{"key":"2025102222544386800_ref007","article-title":"An image is worth 16x16 words: transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"issue":"2","key":"2025102222544386800_ref008","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (voc) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"International Journal of Computer Vision"},{"key":"2025102222544386800_ref009","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"issue":"1","key":"2025102222544386800_ref010","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1108\/ijicc-03-2023-0053","article-title":"BFFNet: a bidirectional feature fusion network for semantic segmentation of remote sensing objects","volume":"17","author":"Hou","year":"2024","journal-title":"International Journal of Intelligent Computing and Cybernetics"},{"key":"2025102222544386800_ref011","unstructured":"Jocher, G., Nishimura, K., Mineeva, T. and Vilarino, R. 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