{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T15:48:04Z","timestamp":1772725684682,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Basic Research Program of China; the Major Project of Science and Technology of Henan Province;  the Key Scientific and Technological Project of Henan Province; the Key Research and Promotion Projects of Henan Province; Kaifeng science and techno","award":["2019YFE0126600; 201400210300; 212102210496; 212102210393; 2002001"],"award-info":[{"award-number":["2019YFE0126600; 201400210300; 212102210496; 212102210393; 2002001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>In recent years, significant progress has been obtained in object detection using Convolutional Neural Networks (CNNs). However, owing to the particularity of Remote Sensing Images (RSIs), common object detection methods are not well suited for RSIs. Aiming at the difficulties in RSIs, this paper proposes an object detection method based on the Dense Feature Fusion Path Aggregation Network (DFF-PANet). Firstly, for better improving the detection performance of small and medium-sized instances, we propose Feature Reuse Module (FRM), which can integrate semantic and location information contained in feature maps; this module can reuse feature maps in the backbone to enhance the detection capability of small and medium-sized instances. After that, we design the DFF-PANet, which can help feature information extracted from the backbone to be fused more efficiently, and thus cope with the problem of external interference factors. We performed experiments on the Dataset of Object deTection in Aerial images (DOTA) dataset and the HRSC2016 dataset; the accuracy reached 71.5% mAP, which exceeds most object detectors of one-stage and two-stages at present. Meanwhile, the size of our model is only 9.2 M, which satisfies the requirement of being lightweight. The experimental results demonstrate that our method not only has better detection accuracy but also maintains high efficiency in RSIs.<\/jats:p>","DOI":"10.3390\/ijgi11030189","type":"journal-article","created":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T12:58:36Z","timestamp":1647003516000},"page":"189","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A Lightweight Object Detection Method in Aerial Images Based on Dense Feature Fusion Path Aggregation Network"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8741-0827","authenticated-orcid":false,"given":"Liming","family":"Zhou","sequence":"first","affiliation":[{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475000, China"},{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5641-9087","authenticated-orcid":false,"given":"Xiaohan","family":"Rao","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475000, China"},{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yahui","family":"Li","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475000, China"},{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianyu","family":"Zuo","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475000, China"},{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baojun","family":"Qiao","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475000, China"},{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinghao","family":"Lin","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475000, China"},{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fu, G., Liu, C.J., Zhou, R., Sun, T., and Zhang, Q.J. 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