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Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image

Wu, Wentong ; Liu, Han ; Li, Lingling ; Long, Yilin ; Wang, Xiaodong ; Wang, Zhuohua ; Li, Jinglun ; Chang, Yi Lv, Haibin

PloS one, 2021-10, Vol.16 (10), p.e0259283-e0259283 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image
Autor: Wu, Wentong ; Liu, Han ; Li, Lingling ; Long, Yilin ; Wang, Xiaodong ; Wang, Zhuohua ; Li, Jinglun ; Chang, Yi
Lv, Haibin
Assuntos: Accuracy ; Adaptability ; Algorithms ; Analysis ; Artificial intelligence ; Artificial neural networks ; Biology and Life Sciences ; Computer and Information Sciences ; Computer architecture ; Datasets ; Earth Sciences ; Engineering and Technology ; Feature extraction ; Feature recognition ; Genetic algorithms ; Geographic information systems ; Image detection ; Neural networks ; Neural Networks, Computer ; Object recognition ; Pattern Recognition, Automated - methods ; Pattern Recognition, Automated - standards ; Physical Sciences ; R&D ; Remote sensing ; Research & development ; Research and Analysis Methods ; Satellite Imagery - methods ; Satellite Imagery - standards ; Satellites ; Signal processing ; Social Sciences ; Storage tanks ; Target detection ; Workloads
É parte de: PloS one, 2021-10, Vol.16 (10), p.e0259283-e0259283
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ObjectType-Correction/Retraction-3
Competing Interests: The authors have declared that no competing interests exist.
Descrição: This exploration primarily aims to jointly apply the local FCN (fully convolution neural network) and YOLO-v5 (You Only Look Once-v5) to the detection of small targets in remote sensing images. Firstly, the application effects of R-CNN (Region-Convolutional Neural Network), FRCN (Fast Region-Convolutional Neural Network), and R-FCN (Region-Based-Fully Convolutional Network) in image feature extraction are analyzed after introducing the relevant region proposal network. Secondly, YOLO-v5 algorithm is established on the basis of YOLO algorithm. Besides, the multi-scale anchor mechanism of Faster R-CNN is utilized to improve the detection ability of YOLO-v5 algorithm for small targets in the image in the process of image detection, and realize the high adaptability of YOLO-v5 algorithm to different sizes of images. Finally, the proposed detection method YOLO-v5 algorithm + R-FCN is compared with other algorithms in NWPU VHR-10 data set and Vaihingen data set. The experimental results show that the YOLO-v5 + R-FCN detection method has the optimal detection ability among many algorithms, especially for small targets in remote sensing images such as tennis courts, vehicles, and storage tanks. Moreover, the YOLO-v5 + R-FCN detection method can achieve high recall rates for different types of small targets. Furthermore, due to the deeper network architecture, the YOL v5 + R-FCN detection method has a stronger ability to extract the characteristics of image targets in the detection of remote sensing images. Meanwhile, it can achieve more accurate feature recognition and detection performance for the densely arranged target images in remote sensing images. This research can provide reference for the application of remote sensing technology in China, and promote the application of satellites for target detection tasks in related fields.
Editor: United States: Public Library of Science
Idioma: Inglês

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Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image

Wu, Wentong ; Liu, Han ; Li, Lingling ; Long, Yilin ; Wang, Xiaodong ; Wang, Zhuohua ; Li, Jinglun ; Chang, Yi Lv, Haibin

United States: Public Library of Science

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