Health evaluation and dangerous reptile detection using a novel framework powered by the YOLO algorithm to design high‐content cellular imaging systems
S. Pandey, Ankit Kumar, D. P. Yadav, Anurag Sinha, M. Hassan, N. K. Singh, Yash Bhatnagar, Namit Garg
{"title":"Health evaluation and dangerous reptile detection using a novel framework powered by the YOLO algorithm to design high‐content cellular imaging systems","authors":"S. Pandey, Ankit Kumar, D. P. Yadav, Anurag Sinha, M. Hassan, N. K. Singh, Yash Bhatnagar, Namit Garg","doi":"10.1049/tje2.12335","DOIUrl":null,"url":null,"abstract":"This novel approach in animal biology could revolutionize identifying endangered species, addressing the issue of misclassifying potentially harmful animals based solely on visual characteristics. Particularly impactful for farmers in agricultural fields, it aims to reduce the heightened risk of venomous animal attacks, ultimately improving safety. Due to a lack of accessible education, illiterate farmers are more susceptible to adopting superstitious beliefs, which tragically leads to fatal snakebites even when medical treatment is readily available. Furthermore, environmental factors can unexpectedly hold typically non‐threatening animals responsible for a large number of human deaths each year. However, the complexity of human recognition of these hazards has prompted the development of a novel design approach aimed at simplifying the process. Integration of the ResNet learning algorithm in conjunction with You Only Look Once (YOLOv5) within the framework is recommended to facilitate real‐time processing and improve accuracy. This combined approach not only speeds up animal recognition but also takes advantage of ResNet's deep learning capabilities. The first phase entails deploying YOLOv5 to detect the presence of snakes in the proposed study, achieving a remarkable 87% precision in snake detection thanks to the synergistic fusion of ResNet and YOLOv5.","PeriodicalId":22858,"journal":{"name":"The Journal of Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Journal of Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1049/tje2.12335","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This novel approach in animal biology could revolutionize identifying endangered species, addressing the issue of misclassifying potentially harmful animals based solely on visual characteristics. Particularly impactful for farmers in agricultural fields, it aims to reduce the heightened risk of venomous animal attacks, ultimately improving safety. Due to a lack of accessible education, illiterate farmers are more susceptible to adopting superstitious beliefs, which tragically leads to fatal snakebites even when medical treatment is readily available. Furthermore, environmental factors can unexpectedly hold typically non‐threatening animals responsible for a large number of human deaths each year. However, the complexity of human recognition of these hazards has prompted the development of a novel design approach aimed at simplifying the process. Integration of the ResNet learning algorithm in conjunction with You Only Look Once (YOLOv5) within the framework is recommended to facilitate real‐time processing and improve accuracy. This combined approach not only speeds up animal recognition but also takes advantage of ResNet's deep learning capabilities. The first phase entails deploying YOLOv5 to detect the presence of snakes in the proposed study, achieving a remarkable 87% precision in snake detection thanks to the synergistic fusion of ResNet and YOLOv5.
这种动物生物学上的新方法可以彻底改变濒危物种的识别,解决仅仅基于视觉特征对潜在有害动物进行错误分类的问题。它对农业领域的农民尤其有影响力,旨在降低有毒动物袭击的高风险,最终提高安全性。由于缺乏接受教育的机会,不识字的农民更容易接受迷信信仰,即使有现成的治疗方法,也不幸导致致命的蛇咬伤。此外,环境因素可能出人意料地使通常不具威胁性的动物对每年大量的人类死亡负责。然而,人类识别这些危险的复杂性促使了一种新的设计方法的发展,旨在简化这一过程。建议将ResNet学习算法与框架内的You Only Look Once (YOLOv5)相结合,以促进实时处理并提高准确性。这种组合方法不仅加快了动物识别速度,而且利用了ResNet的深度学习能力。第一阶段需要部署YOLOv5来检测拟议研究中的蛇的存在,由于ResNet和YOLOv5的协同融合,蛇的检测精度达到了惊人的87%。