@inproceedings{8ee5ef9c38694a95ae2a0e65cfada83c,
title = "Weak Weld-target Recognition Based on Prior Knowledge",
abstract = "The welding technology is widely used in many manufacturing industries. The welding seams are weak targets due to unstable welding quality and the various kinds of noises. This article proposed a new method of weak-target recognition focus on welding seams, which helps to automatically track the welding seam in complex enviroment. We first relabel the data with 'outer boxes' and 'slopes' on the welding seam. Then we design a new recognition framework based on prior knowledge and YOLOv5 recognition algorithm. We trained weld images in many kinds of enviroment and compared the results. It shown that our method exceed the tradional method on both precision and recall.",
keywords = "YOLOv5, feature extraction, prior knowledge, weak-object recognition, welding recognition",
author = "Hongbin Ma and Yidong Xu and Jie Liu",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 4th International Conference on Control and Robotics, ICCR 2022 ; Conference date: 02-12-2022 Through 04-12-2022",
year = "2022",
doi = "10.1109/ICCR55715.2022.10053910",
language = "English",
series = "2022 4th International Conference on Control and Robotics, ICCR 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "393--397",
booktitle = "2022 4th International Conference on Control and Robotics, ICCR 2022",
address = "United States",
}