@inproceedings{e3d6c4c67fb349a4bd420cb3910d759f,
title = "Fast and High Precision Surface Defect Detection Method Based on New Label Assignment",
abstract = "Current object detection networks suffer from low accuracy and slow speed for industrial defect detection tasks. Industrial defect detection tasks are characterized by small area and large aspect ratio of the detected objects, as well as high speed requirements. We provide a label assignment strategy for defect shape characteristics to improve the training efficiency of a one-stage target detection network for defect detection scenarios. Also, label assignment distillation learning is used to obtain a model that takes into account the speed of detection. In this paper, experiments are conducted on several industrial defect datasets, and metrics such as mAP (mean average precision) values and inference speed are calculated. Compared with other models, the label assignment algorithm results in a 3\% improvement in detection accuracy and a 58\% acceleration in model inference speed after lightweighting.",
keywords = "deep learning, defect, detection, distillation learning, label assignment",
author = "Ruitao Li and Xiaojun Wu and Jinghui Zhou and Jiarui Zheng",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023 ; Conference date: 17-07-2023 Through 20-07-2023",
year = "2023",
doi = "10.1109/RCAR58764.2023.10249914",
language = "英语",
series = "Proceedings of the 2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "257--262",
booktitle = "Proceedings of the 2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023",
address = "美国",
}