Drogue detection is a fundamental issue during the close docking phase of autonomous aerial refueling（AAR）. To cope with this issue, a novel and effective method based on deep learning with convolutional neural netw...Drogue detection is a fundamental issue during the close docking phase of autonomous aerial refueling（AAR）. To cope with this issue, a novel and effective method based on deep learning with convolutional neural networks（CNNs） is proposed. In order to ensure its robustness and wide application, a deep learning dataset of images was prepared by utilizing real data of ‘‘Probe and Drogue＂ aerial refueling, which contains diverse drogues in various environmental conditions without artificial features placed on the drogues. By employing deep learning ideas and graphics processing units（GPUs）, a model for drogue detection using a Caffe deep learning framework with CNNs was designed to ensure the method＇s accuracy and real-time performance. Experiments were conducted to demonstrate the effectiveness of the proposed method, and results based on real AAR data compare its performance to other methods, validating the accuracy, speed, and robustness of its drogue detection ability.展开更多
Drogue recognition and 3D locating is a key problem during the docking phase of the autonomous aerial refueling （AAR）. To solve this problem, a novel and effective method based on monocular vision is presented in th...Drogue recognition and 3D locating is a key problem during the docking phase of the autonomous aerial refueling （AAR）. To solve this problem, a novel and effective method based on monocular vision is presented in this paper. Firstly, by employing computer vision with red-ring-shape feature, a drogue detection and recognition algorithm is proposed to guarantee safety and ensure the robustness to the drogue diversity and the changes in environmental condi- tions, without using a set of infrared light emitting diodes （LEDs） on the parachute part of the dro- gue. Secondly, considering camera lens distortion, a monocular vision measurement algorithm for drogue 3D locating is designed to ensure the accuracy and real-time performance of the system, with the drogue attitude provided. Finally, experiments are conducted to demonstrate the effective- ness of the proposed method. Experimental results show the performances of the entire system in contrast with other methods, which validates that the proposed method can recognize and locate the drogue three dimensionally, rapidly and precisely.展开更多
基金co-supported by the National Basic Research Program of China (Nos. 2012CB316301, 2013CB329403)the National Natural Science Foundation of China (Nos. 61473307, 61304120, 61273023, 61332007)
文摘Drogue detection is a fundamental issue during the close docking phase of autonomous aerial refueling（AAR）. To cope with this issue, a novel and effective method based on deep learning with convolutional neural networks（CNNs） is proposed. In order to ensure its robustness and wide application, a deep learning dataset of images was prepared by utilizing real data of ‘‘Probe and Drogue＂ aerial refueling, which contains diverse drogues in various environmental conditions without artificial features placed on the drogues. By employing deep learning ideas and graphics processing units（GPUs）, a model for drogue detection using a Caffe deep learning framework with CNNs was designed to ensure the method＇s accuracy and real-time performance. Experiments were conducted to demonstrate the effectiveness of the proposed method, and results based on real AAR data compare its performance to other methods, validating the accuracy, speed, and robustness of its drogue detection ability.
基金supported by the National Natural Science Foundation of China(Nos.61473307,61304120)
文摘Drogue recognition and 3D locating is a key problem during the docking phase of the autonomous aerial refueling （AAR）. To solve this problem, a novel and effective method based on monocular vision is presented in this paper. Firstly, by employing computer vision with red-ring-shape feature, a drogue detection and recognition algorithm is proposed to guarantee safety and ensure the robustness to the drogue diversity and the changes in environmental condi- tions, without using a set of infrared light emitting diodes （LEDs） on the parachute part of the dro- gue. Secondly, considering camera lens distortion, a monocular vision measurement algorithm for drogue 3D locating is designed to ensure the accuracy and real-time performance of the system, with the drogue attitude provided. Finally, experiments are conducted to demonstrate the effective- ness of the proposed method. Experimental results show the performances of the entire system in contrast with other methods, which validates that the proposed method can recognize and locate the drogue three dimensionally, rapidly and precisely.
