In order to solve the problems such as manual involvement,slow recognition and poor accuracy in elevator positioning in drilling automation,a binocular vision based elevator automatic recognition and orientation detection system was built.By means of building a binocular camera visual detection system,the obtained elevator images were calibrated,stereo corrected,and stereo matched to obtain depth maps of them.A convolutional atten-tion mechanism module was introduced into the backbone network of the original YOLOv5s target detection algo-rithm to strengthen the target to be recognized,i.e.,the elevator image,further improve the recognition accuracy of elevator,and figure out the distance and deflection angle of the elevator center position relative to the manipula-tor combined with depth maps,thus achieving automatic pipe feeding.The effectiveness of elevator recognition and detection method was verified through tests on drilling platforms.The conclusions provide technical reference for further improving the automation level of drilling platforms.
关键词
吊卡识别/方位检测/双目相机/YOLOv5s算法/卷积注意力机制模块/试验验证
Key words
elevator recognition/orientation detection/binocular camera/YOLOv5s algorithm/convolu-tional attention mechanism module/experimental verification
维普
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