A contextual deep neural network with dilated convolutions for object detection in remote sensing images

2018 ◽  
Author(s):  
Xingyue Li ◽  
Shouhong Wan ◽  
Peiquan Jin ◽  
Chang Zou
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Object Detection ◽  
Deep Neural Network ◽  
Remote Sensing Images

scholarly journals Effective Complex Airport Object Detection in Remote Sensing Images Based on Improved End-to-End Convolutional Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 172652-172663
Author(s):  
Yongsai Han ◽  
Shiping Ma ◽  
Yuelei Xu ◽  
Linyuan He ◽  
Shuai Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Remote Sensing Images ◽  
End To End

scholarly journals IoU-Adaptive Deformable R-CNN: Make Full Use of IoU for Multi-Class Object Detection in Remote Sensing Imagery

2019 ◽  
Vol 11 (3) ◽  
pp. 286 ◽  
Author(s):  
Jiangqiao Yan ◽  
Hongqi Wang ◽  
Menglong Yan ◽  
Wenhui Diao ◽  
Xian Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Ground Truth ◽  
Detection Performance ◽  
Candidate Region ◽  
Detection Accuracy ◽  
Remote Sensing Images

Recently, methods based on Faster region-based convolutional neural network (R-CNN)have been popular in multi-class object detection in remote sensing images due to their outstandingdetection performance. The methods generally propose candidate region of interests (ROIs) througha region propose network (RPN), and the regions with high enough intersection-over-union (IoU)values against ground truth are treated as positive samples for training. In this paper, we find thatthe detection result of such methods is sensitive to the adaption of different IoU thresholds. Specially,detection performance of small objects is poor when choosing a normal higher threshold, while alower threshold will result in poor location accuracy caused by a large quantity of false positives.To address the above issues, we propose a novel IoU-Adaptive Deformable R-CNN framework formulti-class object detection. Specially, by analyzing the different roles that IoU can play in differentparts of the network, we propose an IoU-guided detection framework to reduce the loss of small objectinformation during training. Besides, the IoU-based weighted loss is designed, which can learn theIoU information of positive ROIs to improve the detection accuracy effectively. Finally, the class aspectratio constrained non-maximum suppression (CARC-NMS) is proposed, which further improves theprecision of the results. Extensive experiments validate the effectiveness of our approach and weachieve state-of-the-art detection performance on the DOTA dataset.

scholarly journals Ship Object Detection of Remote Sensing Image Based on Visual Attention

2021 ◽  
Vol 13 (16) ◽  
pp. 3192
Author(s):  
Yuxin Dong ◽  
Fukun Chen ◽  
Shuang Han ◽  
Hao Liu
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Visual Attention ◽  
Object Detection ◽  
Remote Sensing Image ◽  
Attention Mechanism ◽  
Remote Sensing Images ◽  
Data Set ◽  
Ship Detection ◽  
Visual Attention Mechanism

At present, reliable and precise ship detection in high-resolution optical remote sensing images affected by wave clutter, thin clouds, and islands under complex sea conditions is still challenging. At the same time, object detection algorithms in satellite remote sensing images are challenged by color, aspect ratio, complex background, and angle variability. Even the results obtained based on the latest convolutional neural network (CNN) method are not satisfactory. In order to obtain more accurate ship detection results, this paper proposes a remote sensing image ship object detection method based on a brainlike visual attention mechanism. We refer to the robust expression mode of the human brain, design a vector field filter with active rotation capability, and explicitly encode the direction information of the remote sensing object in the neural network. The progressive enhancement learning model guided by the visual attention mechanism is used to dynamically solve the problem, and the object can be discovered and detected through time–space information. To verify the effectiveness of the proposed method, a remote sensing ship object detection data set is established, and the proposed method is compared with other state-of-the-art methods on the established data set. Experiments show that the object detection accuracy of this method and the ability to capture image details have been improved. Compared with other models, the average intersection rate of the joint is 80.12%, which shows a clear advantage. The proposed method is fast enough to meet the needs of ship detection in remote sensing images.

Region-Enhanced Convolutional Neural Network for Object Detection in Remote Sensing Images

2020 ◽  
Vol 58 (8) ◽  
pp. 5693-5702 ◽  
Author(s):  
Jianjun Lei ◽  
Xiaowei Luo ◽  
Leyuan Fang ◽  
Mengyuan Wang ◽  
Yanfeng Gu
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Remote Sensing Images
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