Advancing low‐light object detection with you only look once models: An empirical study and performance evaluation
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 stat...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Cognitive Computation and Systems |
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| Online Access: | https://doi.org/10.1049/ccs2.12114 |
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| author | Samier Uddin Ahammad Shovo Md. Golam Rabbani Abir Md. Mohsin Kabir M. F. Mridha |
| author_facet | Samier Uddin Ahammad Shovo Md. Golam Rabbani Abir Md. Mohsin Kabir M. F. Mridha |
| author_sort | Samier Uddin Ahammad Shovo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b601bccf83014560a4c3a22cc1e4358e |
| institution | Kabale University |
| issn | 2517-7567 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cognitive Computation and Systems |
| spelling | doaj-art-b601bccf83014560a4c3a22cc1e4358e2024-12-26T15:31:31ZengWileyCognitive Computation and Systems2517-75672024-12-016411913410.1049/ccs2.12114Advancing low‐light object detection with you only look once models: An empirical study and performance evaluationSamier Uddin Ahammad Shovo0Md. Golam Rabbani Abir1Md. Mohsin Kabir2M. F. Mridha3Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka BangladeshDepartment of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka BangladeshDepartment of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka BangladeshDepartment of Computer Science American International University‐Bangladesh Dhaka BangladeshAbstract 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.https://doi.org/10.1049/ccs2.12114artificial neural networksmachine learningmachine vision |
| spellingShingle | Samier Uddin Ahammad Shovo Md. Golam Rabbani Abir Md. Mohsin Kabir M. F. Mridha Advancing low‐light object detection with you only look once models: An empirical study and performance evaluation Cognitive Computation and Systems artificial neural networks machine learning machine vision |
| title | Advancing low‐light object detection with you only look once models: An empirical study and performance evaluation |
| title_full | Advancing low‐light object detection with you only look once models: An empirical study and performance evaluation |
| title_fullStr | Advancing low‐light object detection with you only look once models: An empirical study and performance evaluation |
| title_full_unstemmed | Advancing low‐light object detection with you only look once models: An empirical study and performance evaluation |
| title_short | Advancing low‐light object detection with you only look once models: An empirical study and performance evaluation |
| title_sort | advancing low light object detection with you only look once models an empirical study and performance evaluation |
| topic | artificial neural networks machine learning machine vision |
| url | https://doi.org/10.1049/ccs2.12114 |
| work_keys_str_mv | AT samieruddinahammadshovo advancinglowlightobjectdetectionwithyouonlylookoncemodelsanempiricalstudyandperformanceevaluation AT mdgolamrabbaniabir advancinglowlightobjectdetectionwithyouonlylookoncemodelsanempiricalstudyandperformanceevaluation AT mdmohsinkabir advancinglowlightobjectdetectionwithyouonlylookoncemodelsanempiricalstudyandperformanceevaluation AT mfmridha advancinglowlightobjectdetectionwithyouonlylookoncemodelsanempiricalstudyandperformanceevaluation |