Dual-NMS: A method for autonomously removing false detection boxes from aerial image object detection results
文献类型:期刊论文
作者 | Lin ZY(林智远)1,2,3,4,5; Wu QX(吴清潇)1,2,4,5; Fu SF(付双飞)1,2,4,5; Wang SK(王思奎)1,2,3,4,5; Zhang ZY(张钟毓)1,2,3,4,5; Kong YZ(孔研自)1,2,3,4,5 |
刊名 | SENSORS |
出版日期 | 2019 |
卷号 | 19期号:21页码:1-18 |
ISSN号 | 1424-8220 |
关键词 | false detection boxes density of detection boxes dual-NMS object detection aerial image deep learning |
产权排序 | 1 |
英文摘要 | In the field of aerial image object detection based on deep learning, it’s 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’s 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. |
资助项目 | National Natural Science Foundation of China[U1713216] |
WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
语种 | 英语 |
WOS记录号 | WOS:000498834000088 |
资助机构 | National Natural Science Foundation of China (U1713216) |
源URL | [http://ir.sia.cn/handle/173321/25870] |
专题 | 沈阳自动化研究所_光电信息技术研究室 |
通讯作者 | Lin ZY(林智远) |
作者单位 | 1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 2.The Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China 3.University of Chinese Academy of Sciences, Beijing 100049, China 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 5.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China |
推荐引用方式 GB/T 7714 | Lin ZY,Wu QX,Fu SF,et al. Dual-NMS: A method for autonomously removing false detection boxes from aerial image object detection results[J]. SENSORS,2019,19(21):1-18. |
APA | Lin ZY,Wu QX,Fu SF,Wang SK,Zhang ZY,&Kong YZ.(2019).Dual-NMS: A method for autonomously removing false detection boxes from aerial image object detection results.SENSORS,19(21),1-18. |
MLA | Lin ZY,et al."Dual-NMS: A method for autonomously removing false detection boxes from aerial image object detection results".SENSORS 19.21(2019):1-18. |
入库方式: OAI收割
来源:沈阳自动化研究所
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