基于目标识别和图像处理的热工检测算法研究

Guinan Zhao, Lei Li, Xiaodong Liu, Ran Zhao, Wenjie Tang, Fangji Gan
{"title":"基于目标识别和图像处理的热工检测算法研究","authors":"Guinan Zhao, Lei Li, Xiaodong Liu, Ran Zhao, Wenjie Tang, Fangji Gan","doi":"10.1109/ICPECA60615.2024.10471113","DOIUrl":null,"url":null,"abstract":"To enable real-time monitoring of fire operations within construction sites and to reduce the chance of fires, this paper proposes a detection algorithm that incorporates target recognition and image processing. Firstly, we add an attention mechanism to YOLO V7 to increase the localization accuracy of highlighted areas. Secondly, we use an image processing algorithm to identify the presence of fire in the localized area and recheck the localized highlighted area to improve the detection accuracy. After the verification is complete, this paper proposes a method based on monocular visual ranging to detect the presence of staff around the fire light to further confirm the authenticity of the fire operation and finally complete the detection of the fire moving operation. To verify the reliability and recognition accuracy of the algorithm, experiments were conducted on the dataset collected at the construction site, including ablation experiments on different target recognition networks and the application of different attention mechanisms. In addition to experiments on image segmentation of different highlighted areas located, and finally, extensive verification experiments on monocular visual ranging experiments are carried out. The experimental results show that the algorithm enables an effective detection of fire operations.","PeriodicalId":518671,"journal":{"name":"2024 IEEE 4th International Conference on Power, Electronics and Computer Applications (ICPECA)","volume":"95 4","pages":"1179-1186"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Detection Algorithm of Hot Work Based on Target Recognition and Image Processing\",\"authors\":\"Guinan Zhao, Lei Li, Xiaodong Liu, Ran Zhao, Wenjie Tang, Fangji Gan\",\"doi\":\"10.1109/ICPECA60615.2024.10471113\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To enable real-time monitoring of fire operations within construction sites and to reduce the chance of fires, this paper proposes a detection algorithm that incorporates target recognition and image processing. Firstly, we add an attention mechanism to YOLO V7 to increase the localization accuracy of highlighted areas. Secondly, we use an image processing algorithm to identify the presence of fire in the localized area and recheck the localized highlighted area to improve the detection accuracy. After the verification is complete, this paper proposes a method based on monocular visual ranging to detect the presence of staff around the fire light to further confirm the authenticity of the fire operation and finally complete the detection of the fire moving operation. To verify the reliability and recognition accuracy of the algorithm, experiments were conducted on the dataset collected at the construction site, including ablation experiments on different target recognition networks and the application of different attention mechanisms. In addition to experiments on image segmentation of different highlighted areas located, and finally, extensive verification experiments on monocular visual ranging experiments are carried out. The experimental results show that the algorithm enables an effective detection of fire operations.\",\"PeriodicalId\":518671,\"journal\":{\"name\":\"2024 IEEE 4th International Conference on Power, Electronics and Computer Applications (ICPECA)\",\"volume\":\"95 4\",\"pages\":\"1179-1186\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2024 IEEE 4th International Conference on Power, Electronics and Computer Applications (ICPECA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPECA60615.2024.10471113\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2024 IEEE 4th International Conference on Power, Electronics and Computer Applications (ICPECA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPECA60615.2024.10471113","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

为了能够实时监控建筑工地内的消防作业,减少火灾发生的几率,本文提出了一种结合目标识别和图像处理的检测算法。首先,我们在 YOLO V7 中添加了关注机制,以提高高亮区域的定位精度。其次,我们使用图像处理算法来识别定位区域中是否存在火灾,并对定位的高亮区域进行复核,以提高检测精度。验证完成后,本文提出一种基于单目视觉测距的方法,检测火光周围是否存在工作人员,进一步确认动火作业的真实性,最终完成动火作业的检测。为了验证算法的可靠性和识别准确性,在建筑工地采集的数据集上进行了实验,包括不同目标识别网络的消融实验和不同注意机制的应用实验。此外,还对所定位的不同高亮区域进行了图像分割实验,最后在单目视觉测距实验中进行了大量验证实验。实验结果表明,该算法能够有效地检测火灾行动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Detection Algorithm of Hot Work Based on Target Recognition and Image Processing
To enable real-time monitoring of fire operations within construction sites and to reduce the chance of fires, this paper proposes a detection algorithm that incorporates target recognition and image processing. Firstly, we add an attention mechanism to YOLO V7 to increase the localization accuracy of highlighted areas. Secondly, we use an image processing algorithm to identify the presence of fire in the localized area and recheck the localized highlighted area to improve the detection accuracy. After the verification is complete, this paper proposes a method based on monocular visual ranging to detect the presence of staff around the fire light to further confirm the authenticity of the fire operation and finally complete the detection of the fire moving operation. To verify the reliability and recognition accuracy of the algorithm, experiments were conducted on the dataset collected at the construction site, including ablation experiments on different target recognition networks and the application of different attention mechanisms. In addition to experiments on image segmentation of different highlighted areas located, and finally, extensive verification experiments on monocular visual ranging experiments are carried out. The experimental results show that the algorithm enables an effective detection of fire operations.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信