Focusing on the airplanes in remote-sensing images, a real-time algorithm based on improved YOLOv3 is proposed to detect airplanes in remote-sensing images. Firstly, a convolutional neural network that consists of 49 convolutional layers is proposed to detect airplanes in remote-sensing images specifically. Secondly, dense connection is employed on proposed convolutional neural network, and maxpool is employed to enhance the feature transmit between dense blocks. Finally, to deal with the fact that airplanes in remote-sensing images are small targets mainly, we propose to increase the scale detection from 3 to 4 and employ dense connection to merge feature map among different scales. The algorithm is trained and tested on the designed airplane dataset. The experiment results show that our algorithm obtain 96.26% on precision and 93.81% on recall.
Real-time airplane detection algorithm in remote-sensing images based on improved YOLOv3
First published at:Dec 01, 2018
Opto-Electronic Engineering Vol. 45, Issue 12, pp. 180350 (2018)
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Supported by National High Technology Research and Development Program ("863"Program) of China (863-2-5-1-13B)
Get Citation: Dai Weicong, Jin Longxu, Li Guoning, et al. Real-time airplane detection algorithm in remote-sensing images based on improved YOLOv3[J]. Opto-Electronic Engineering, 2018, 45(12): 180350.
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