scholarly journals An Aircraft Object Detection Algorithm Based on Small Samples in Optical Remote Sensing Image

2020 ◽  
Vol 10 (17) ◽  
pp. 5778
Author(s):  
Ting Wang ◽  
Changqing Cao ◽  
Xiaodong Zeng ◽  
Zhejun Feng ◽  
Jingshi Shen ◽  
...  

In recent years, remote sensing technology has developed rapidly, and the ground resolution of spaceborne optical remote sensing images has reached the sub-meter range, providing a new technical means for aircraft object detection. Research on aircraft object detection based on optical remote sensing images is of great significance for military object detection and recognition. However, spaceborne optical remote sensing images are difficult to obtain and costly. Therefore, this paper proposes the aircraft detection algorithm, itcan detect aircraft objects with small samples. Firstly, this paper establishes an aircraft object dataset containing weak and small aircraft objects. Secondly, the detection algorithm has been proposed to detect weak and small aircraft objects. Thirdly, the aircraft detection algorithm has been proposed to detect multiple aircraft objects of varying sizes. There are 13,324 aircraft in the test set. According to the method proposed in this paper, the f1 score can achieve 90.44%. Therefore, the aircraft objects can be detected simply and efficiently by using the method proposed. It can effectively detect aircraft objects and improve early warning capabilities.

2021 ◽  
Vol 30 ◽  
pp. 1305-1317
Author(s):  
Qijian Zhang ◽  
Runmin Cong ◽  
Chongyi Li ◽  
Ming-Ming Cheng ◽  
Yuming Fang ◽  
...  

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 20818-20827 ◽  
Author(s):  
Zhi Zhang ◽  
Ruoqiao Jiang ◽  
Shaohui Mei ◽  
Shun Zhang ◽  
Yifan Zhang

2019 ◽  
Vol 12 (1) ◽  
pp. 44 ◽  
Author(s):  
Haojie Ma ◽  
Yalan Liu ◽  
Yuhuan Ren ◽  
Jingxian Yu

An important and effective method for the preliminary mitigation and relief of an earthquake is the rapid estimation of building damage via high spatial resolution remote sensing technology. Traditional object detection methods only use artificially designed shallow features on post-earthquake remote sensing images, which are uncertain and complex background environment and time-consuming feature selection. The satisfactory results from them are often difficult. Therefore, this study aims to apply the object detection method You Only Look Once (YOLOv3) based on the convolutional neural network (CNN) to locate collapsed buildings from post-earthquake remote sensing images. Moreover, YOLOv3 was improved to obtain more effective detection results. First, we replaced the Darknet53 CNN in YOLOv3 with the lightweight CNN ShuffleNet v2. Second, the prediction box center point, XY loss, and prediction box width and height, WH loss, in the loss function was replaced with the generalized intersection over union (GIoU) loss. Experiments performed using the improved YOLOv3 model, with high spatial resolution aerial remote sensing images at resolutions of 0.5 m after the Yushu and Wenchuan earthquakes, show a significant reduction in the number of parameters, detection speed of up to 29.23 f/s, and target precision of 90.89%. Compared with the general YOLOv3, the detection speed improved by 5.21 f/s and its precision improved by 5.24%. Moreover, the improved model had stronger noise immunity capabilities, which indicates a significant improvement in the model’s generalization. Therefore, this improved YOLOv3 model is effective for the detection of collapsed buildings in post-earthquake high-resolution remote sensing images.


2020 ◽  
Vol 415 ◽  
pp. 411-420 ◽  
Author(s):  
Chongyi Li ◽  
Runmin Cong ◽  
Chunle Guo ◽  
Hua Li ◽  
Chunjie Zhang ◽  
...  

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