Power Line Segmentation in Aerial Images Using Convolutional Neural Networks

2019 ◽  
pp. 623-632
Author(s):  
Sumeet Saurav ◽  
Prashant Gidde ◽  
Sanjay Singh ◽  
Ravi Saini
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Power Line ◽  
Aerial Images ◽  
Line Segmentation

Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks

2020 ◽  
Vol 50 (4) ◽  
pp. 1486-1498 ◽  
Author(s):  
Xian Tao ◽  
Dapeng Zhang ◽  
Zihao Wang ◽  
Xilong Liu ◽  
Hongyan Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Power Line ◽  
Aerial Images

Semantic Segmentation of Aerial Images With Shuffling Convolutional Neural Networks

2018 ◽  
Vol 15 (2) ◽  
pp. 173-177 ◽  
Author(s):  
Kaiqiang Chen ◽  
Kun Fu ◽  
Menglong Yan ◽  
Xin Gao ◽  
Xian Sun ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Aerial Images

scholarly journals Multiple-Oriented and Small Object Detection with Convolutional Neural Networks for Aerial Image

2019 ◽  
Vol 11 (18) ◽  
pp. 2176 ◽  
Author(s):  
Chen ◽  
Zhong ◽  
Tan
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Aerial Images ◽  
Superior Performance ◽  
Aerial Image ◽  
Detection Accuracy ◽  
Small Object ◽  
Data Set ◽  
Orientation Information

Detecting objects in aerial images is a challenging task due to multiple orientations and relatively small size of the objects. Although many traditional detection models have demonstrated an acceptable performance by using the imagery pyramid and multiple templates in a sliding-window manner, such techniques are inefficient and costly. Recently, convolutional neural networks (CNNs) have successfully been used for object detection, and they have demonstrated considerably superior performance than that of traditional detection methods; however, this success has not been expanded to aerial images. To overcome such problems, we propose a detection model based on two CNNs. One of the CNNs is designed to propose many object-like regions that are generated from the feature maps of multi scales and hierarchies with the orientation information. Based on such a design, the positioning of small size objects becomes more accurate, and the generated regions with orientation information are more suitable for the objects arranged with arbitrary orientations. Furthermore, another CNN is designed for object recognition; it first extracts the features of each generated region and subsequently makes the final decisions. The results of the extensive experiments performed on the vehicle detection in aerial imagery (VEDAI) and overhead imagery research data set (OIRDS) datasets indicate that the proposed model performs well in terms of not only the detection accuracy but also the detection speed.

scholarly journals Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks

Sensors ◽  
2017 ◽  
Vol 17 (12) ◽  
pp. 2720 ◽  
Author(s):  
Jiandan Zhong ◽  
Tao Lei ◽  
Guangle Yao
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Vehicle Detection ◽  
Aerial Images
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