{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T10:26:20Z","timestamp":1760955980402,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,10,28]],"date-time":"2019-10-28T00:00:00Z","timestamp":1572220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the field of aerial image object detection based on deep learning, it\u2019s difficult to extract features because the images are obtained from a top-down perspective. Therefore, there are numerous false detection boxes. The existing post-processing methods mainly remove overlapped detection boxes, but it\u2019s hard to eliminate false detection boxes. The proposed dual non-maximum suppression (dual-NMS) combines the density of detection boxes that are generated for each detected object with the corresponding classification confidence to autonomously remove the false detection boxes. With the dual-NMS as a post-processing method, the precision is greatly improved under the premise of keeping recall unchanged. In vehicle detection in aerial imagery (VEDAI) and dataset for object detection in aerial images (DOTA) datasets, the removal rate of false detection boxes is over 50%. Additionally, according to the characteristics of aerial images, the correlation calculation layer for feature channel separation and the dilated convolution guidance structure are proposed to enhance the feature extraction ability of the network, and these structures constitute the correlation network (CorrNet). Compared with you only look once (YOLOv3), the mean average precision (mAP) of the CorrNet for DOTA increased by 9.78%. Commingled with dual-NMS, the detection effect in aerial images is significantly improved.<\/jats:p>","DOI":"10.3390\/s19214691","type":"journal-article","created":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T05:18:26Z","timestamp":1572499106000},"page":"4691","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Dual-NMS: A Method for Autonomously Removing False Detection Boxes from Aerial Image Object Detection Results"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6752-3226","authenticated-orcid":false,"given":"Zhiyuan","family":"Lin","sequence":"first","affiliation":[{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"The Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingxiao","family":"Wu","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"The Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuangfei","family":"Fu","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"The Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sikui","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"The Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"The Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanzi","family":"Kong","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"The Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J., and Malik, J. 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