| 何赟泽,谯灵俊,王洪金,马刚,王耀南.基于改进DETR的智慧车间人员典型行为识别算法[J].电子测量与仪器学报,2024,38(9):76-84 |
| 基于改进DETR的智慧车间人员典型行为识别算法 |
| Typical behavior recognition algorithm for intelligentworkshop personnel based on improved DETR |
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| DOI： |
| 中文关键词: DETR 行为识别 注意力机制 深度学习 智慧车间 红外数据集 |
| 英文关键词:DETR behavior recognition attention mechanism deep learning smart workshop infrared dataset |
| 基金项目:湖南省重点研发计划（2022GK2012）、湖南省科技创新领军人才（2023RC1039）、湖南省自然科学基金杰出青年基金项目（2022JJ10017）资助 |
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| Author | Institution |
| He Yunze | School of Electrical and Information Engineering, Hunan University, Changsha 410082, China |
| Qiao Lingjun | School of Electrical and Information Engineering, Hunan University, Changsha 410082, China |
| Wang Hongjin | School of Electrical and Information Engineering, Hunan University, Changsha 410082, China |
| Ma Gang | Hunan Red Solar New Energy Science and Technology Co., Ltd, Changsha 410205, China |
| Wang Yaonan | School of Electrical and Information Engineering, Hunan University, Changsha 410082, China |
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| 中文摘要: |
| 生产车间环境复杂，设备众多且人员活动具有高度自主性和不确定性，传统的人工观测方式在面对海量监控数据时，难以实现高效的实时管控。为提高车间人员行为的自动化监测水平，保障生产安全，提出一种基于改进DETR的行为识别算法。通过智慧车间的实地调研，采集多种工作行为及异常行为数据，构建车间红外行为数据集，并在此基础上设计改进算法。针对原始算法的不足，引入相对位置编码，并采用空间调制共同注意力机制，旨在提升网络对全局特征中待检测物体的定位精度。此外，通过引入待检测物体的高斯分布权重，使网络解码器更加高效地识别行为特征。实验结果表明，改进后的算法在自建数据集上的识别精度相比原始算法提高了6.97%，并在公开数据集上同样表现出色。该改进方法不仅为车间人员行为的监控提供了更加高效的解决方案，也为智慧车间的自动化与智能化发展提供了有力的技术支持。 |
| 英文摘要: |
| The production workshop environment is complex, with numerous equipment and highly autonomous and uncertain personnel activities. Traditional manual observation methods are difficult to achieve efficient real-time control when facing massive monitoring data. To improve the automation monitoring level of workshop personnel behavior and ensure production safety, a behavior recognition algorithm based on improved DETR is proposed. Through on-site research in the smart workshop, various work behavior and abnormal behavior data were collected to construct an infrared behavior dataset for the workshop, and an improved algorithm was designed based on this. In response to the shortcomings of the original algorithm, relative position encoding is introduced and a spatial modulation joint attention mechanism is adopted to improve the network’s localization accuracy of the object to be detected in the global features. In addition, by introducing Gaussian distribution weights of the object to be detected, the network decoder can more efficiently recognize behavioral features. The experimental results show that the improved algorithm has improved recognition accuracy by 6.97% on self built datasets compared to the original algorithm, and also performs well on public datasets. This improvement method not only provides a more efficient solution for monitoring the behavior of workshop personnel, but also provides strong technical support for the automation and intelligent development of smart workshops. |
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