A Robust Insulator Detection Algorithm Based on Local Features and Spatial Orders for Aerial Images

The detection of targets with complex backgrounds in aerial images is a challenging task. In this letter, we propose a robust insulator detection algorithm based on local features and spatial orders for aerial images. First, we detect local features and introduce a multiscale and multifeature descriptor to represent the local features. Then, we get several spatial orders features by training these local features, it improves the robustness of the algorithm. Finally, through a coarse-to-fine matching strategy, we eliminate background noise and determine the region of insulators. We test our method on a diverse aerial image set. The experimental results demonstrate the precision and robustness of our detection method, and indicate the possible use of our method in practical applications.

[1]  Cordelia Schmid,et al.  A sparse texture representation using local affine regions , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Cem Ünsalan,et al.  Urban Area Detection Using Local Feature Points and Spatial Voting , 2010, IEEE Geoscience and Remote Sensing Letters.

[3]  Horst Bischof,et al.  Visual Recognition and Fault Detection for Power Line Insulators , 2014 .

[4]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[5]  Vincent Lepetit,et al.  A fast local descriptor for dense matching , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Shimon Ullman,et al.  Object recognition with informative features and linear classification , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[7]  Jubai An,et al.  A Simple and Robust Feature Point Matching Algorithm Based on Restricted Spatial Order Constraints for Aerial Image Registration , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Cordelia Schmid,et al.  Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[9]  Jian Zhang,et al.  Insulators Recognition for 220kv/330kv High-voltage Live-line Cleaning Robot , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[10]  Jacob Goldberger,et al.  Urban-Area Segmentation Using Visual Words , 2009, IEEE Geoscience and Remote Sensing Letters.

[11]  D. Tegolo,et al.  Improving Harris corner selection strategy , 2011 .

[12]  Dan Roth,et al.  Learning a Sparse Representation for Object Detection , 2002, ECCV.

[13]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[14]  Jubai An,et al.  A Texture Segmentation Algorithm Based on PCA and Global Minimization Active Contour Model for Aerial Insulator Images , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Yu Li,et al.  Automatic Target Detection in High-Resolution Remote Sensing Images Using Spatial Sparse Coding Bag-of-Words Model , 2012, IEEE Geoscience and Remote Sensing Letters.

[16]  Ye Gao-sheng.  Recognition of Insulator Based on Developed MPEG-7 Texture Feature , 2010 .

[17]  Ming Zhao,et al.  Bi-SOGC: A Graph Matching Approach Based on Bilateral KNN Spatial Orders Around Geometric Centers for Remote Sensing Image Registration , 2013, IEEE Geoscience and Remote Sensing Letters.