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Aircraft Detection for Remote Sensing Image Based on Bidirectional and Dense Feature Fusion.
Zhou, Liming; Yan, Haoxin; Zheng, Chang; Rao, Xiaohan; Li, Yahui; Yang, Wencheng; Tian, Junfeng; Fan, Minghu; Zuo, Xianyu.
Afiliação
  • Zhou L; Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, Henan, China.
  • Yan H; School of Computer and Information Engineering, Henan University, Kaifeng, Henan, China.
  • Zheng C; Zhongke Langfang Institute of Spatial Information Applications, Langfang, Hebei, China.
  • Rao X; Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, Henan, China.
  • Li Y; School of Computer and Information Engineering, Henan University, Kaifeng, Henan, China.
  • Yang W; Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, Henan, China.
  • Tian J; School of Computer and Information Engineering, Henan University, Kaifeng, Henan, China.
  • Fan M; Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, Henan, China.
  • Zuo X; School of Computer and Information Engineering, Henan University, Kaifeng, Henan, China.
Comput Intell Neurosci ; 2021: 7618828, 2021.
Article em En | MEDLINE | ID: mdl-34567103
Aircraft, as one of the indispensable transport tools, plays an important role in military activities. Therefore, it is a significant task to locate the aircrafts in the remote sensing images. However, the current object detection methods cause a series of problems when applied to the aircraft detection for the remote sensing image, for instance, the problems of low rate of detection accuracy and high rate of missed detection. To address the problems of low rate of detection accuracy and high rate of missed detection, an object detection method for remote sensing image based on bidirectional and dense feature fusion is proposed to detect aircraft targets in sophisticated environments. On the fundamental of the YOLOv3 detection framework, this method adds a feature fusion module to enrich the details of the feature map by mixing the shallow features with the deep features together. Experimental results on the RSOD-DataSet and NWPU-DataSet indicate that the new method raised in the article is capable of improving the problems of low rate of detection accuracy and high rate of missed detection. Meanwhile, the AP for the aircraft increases by 1.57% compared with YOLOv3.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aeronaves / Tecnologia de Sensoriamento Remoto Tipo de estudo: Diagnostic_studies Idioma: En Revista: Comput Intell Neurosci Assunto da revista: INFORMATICA MEDICA / NEUROLOGIA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China País de publicação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aeronaves / Tecnologia de Sensoriamento Remoto Tipo de estudo: Diagnostic_studies Idioma: En Revista: Comput Intell Neurosci Assunto da revista: INFORMATICA MEDICA / NEUROLOGIA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China País de publicação: Estados Unidos