Advancing low-light object detection with you only look once models: An empirical study and performance evaluation

IF 1.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Samier Uddin Ahammad Shovo, Md. Golam Rabbani Abir, Md. Mohsin Kabir, M. F. Mridha
{"title":"Advancing low-light object detection with you only look once models: An empirical study and performance evaluation","authors":"Samier Uddin Ahammad Shovo,&nbsp;Md. Golam Rabbani Abir,&nbsp;Md. Mohsin Kabir,&nbsp;M. F. Mridha","doi":"10.1049/ccs2.12114","DOIUrl":null,"url":null,"abstract":"<p>Low-light object detection is needed for ensuring security, enabling surveillance, and enhancing safety in diverse applications, including autonomous vehicles, surveillance systems, and search and rescue operations. A comprehensive study on low-light object detection is presented using state-of-the-art you only look once (YOLO) models, including YOLOv3, YOLOv5, YOLOv6, and YOLOv8, aiming to enhance detection performance under challenging low-light conditions. The ExDark dataset is a dataset that consists of adequate low-light images, modified to simulate realistic low-light scenarios, and employed for evaluation. The deep learning algorithm optimises YOLO's architecture for low-light detection by adapting the network structure and training strategies while preserving the algorithm's integrity. The experimental results show that YOLOv8 consistently outperforms baseline models, achieving significant improvements in accuracy and robustness in low-light scenarios. The deep learning algorithm that acquired the best score, YOLOv8s, had a mean average precision score of 0.5513. This work contributes to the field of low-light object detection, offering promising solutions for real-world applications like nighttime surveillance and autonomous navigation in low-light conditions, addressing the growing demand for advanced low-light object detection.</p>","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"6 4","pages":"119-134"},"PeriodicalIF":1.2000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ccs2.12114","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognitive Computation and Systems","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/ccs2.12114","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Low-light object detection is needed for ensuring security, enabling surveillance, and enhancing safety in diverse applications, including autonomous vehicles, surveillance systems, and search and rescue operations. A comprehensive study on low-light object detection is presented using state-of-the-art you only look once (YOLO) models, including YOLOv3, YOLOv5, YOLOv6, and YOLOv8, aiming to enhance detection performance under challenging low-light conditions. The ExDark dataset is a dataset that consists of adequate low-light images, modified to simulate realistic low-light scenarios, and employed for evaluation. The deep learning algorithm optimises YOLO's architecture for low-light detection by adapting the network structure and training strategies while preserving the algorithm's integrity. The experimental results show that YOLOv8 consistently outperforms baseline models, achieving significant improvements in accuracy and robustness in low-light scenarios. The deep learning algorithm that acquired the best score, YOLOv8s, had a mean average precision score of 0.5513. This work contributes to the field of low-light object detection, offering promising solutions for real-world applications like nighttime surveillance and autonomous navigation in low-light conditions, addressing the growing demand for advanced low-light object detection.

Abstract Image

推进低光物体检测与您只看一次模型:实证研究和性能评估
在自动驾驶汽车、监视系统、搜索和救援行动等各种应用中,需要低光物体检测来确保安全、实现监视和提高安全性。本文采用最先进的YOLO模型,包括YOLOv3、YOLOv5、YOLOv6和YOLOv8,对低光目标检测进行了全面研究,旨在提高低光条件下的检测性能。ExDark数据集是一个由足够的低光图像组成的数据集,经过修改以模拟真实的低光场景,并用于评估。深度学习算法通过调整网络结构和训练策略来优化YOLO的低光检测架构,同时保持算法的完整性。实验结果表明,YOLOv8持续优于基线模型,在低光场景下的精度和鲁棒性都有显著提高。获得最佳分数的深度学习算法YOLOv8s的平均精度分数为0.5513。这项工作为低光目标检测领域做出了贡献,为现实世界的夜间监视和低光条件下的自主导航等应用提供了有希望的解决方案,满足了对先进低光目标检测日益增长的需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Cognitive Computation and Systems
Cognitive Computation and Systems Computer Science-Computer Science Applications
CiteScore
2.50
自引率
0.00%
发文量
39
审稿时长
10 weeks
×
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