International Conference on Control and Robotics, ICCR 2023, pp.225 - 229
Type
Conference Paper
ISBN
9798350307610
Abstract
In this paper, we propose a target round bar detection method and two-stage camera calibration method for an automatic label attachment system. The proposed method allows a collaborative robot to automatically and accurately attach labels to round bars. In order to detect target round bars, a round bar detection network is trained by using a round bar image with annotations, and the learned round bar detection network detects round bars in captured images. We propose a hierarchical clustering technique to remove the other round bars except the target round bars. It is important not only to accurately detect the round bars in images, but also to precisely convert the location information in the image into coordinates in the real world. A twostage camera calibration method is proposed to perform precise coordinate transformation. We test and evaluate the proposed method using test images obtained from real sites. The experimental results show that the proposed target round bar detection method provided high detection accuracy and accurate round bar center estimation ability while detecting only the target round bars. They further show that the proposed camera calibration method gives precise coordinate transformation performance through more than hundreds of automatically generated coordinate pairs.
Research Interests
Deep learning;딥러닝; object detection;객체검출; re-identification;재식별; multi-object tracking;다중객체추적; multi-camera video analysis;다중카메라영상분석