Title :
Aerial refueling drogue detection based on sliding-window object detector and hybrid features
Author :
Mingran Bai ; Xingang Wang ; Yingjie Yin ; De Xu
Author_Institution :
Res. Center of Precision Sensing & Control, Inst. of Autom., Beijing, China
Abstract :
In order to present an aerial refueling drogue detector, we use a sliding-window object detection framework, while using hybrid features of the sub-image in the detecting windows. Image processing technique and wavelet filter technique are used in the process of feature extraction, to form hybrid feature set. Feature selection depending on AdaBoost is used in the process of feature subset selection. The detection of the black center of drogue is in conjunction with the determination of the external umbrella area. Our aerial refueling drogue detection works well in a variety of illumination environment as well as works with high computational efficiency.
Keywords :
feature extraction; filtering theory; learning (artificial intelligence); object detection; AdaBoost; aerial refueling drogue detection; black center detection; external umbrella area; feature extraction process; feature subset selection; hybrid features; image processing technique; sliding window object detector; wavelet filter technique; Computer vision; Detectors; Feature extraction; Object detection; Support vector machine classification; Training; Visualization; AdaBoost; Computer Vision; Machine learning; Object detection; Precise control;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2014 11th World Congress on
DOI :
10.1109/WCICA.2014.7052691
