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| Title | Integration Of Yolov9 And Contrast Limited Adaptive Histogram Equalization For Nighttime Traffic Sign Detection |
|---|---|
| ID_Doc | 32231 |
| Authors | Dewi C.; Chernovita H.P.; Philemon S.A.; Ananta C.A.; Dai G.; Chen A.P.S. |
| Year | 2025 |
| Published | Mathematical Modelling of Engineering Problems, 12, 1 |
| DOI | http://dx.doi.org/10.18280/mmep.120105 |
| Abstract | The rapid development and use of artificial intelligence in various industries in recent years have markedly improved transportation systems. Automobile collisions can lead to numerous fatalities and significant financial losses. Automated vehicles can employ road detection as one of their functionalities. Notwithstanding the appalling nature of traffic accidents, numerous nations are employing artificial intelligence to create smart cities and autonomous vehicles. This research concentrates on traffic sign detection at night, building upon significant studies conducted by numerous researchers utilizing public road sign data sets. This dataset is essential for training autonomous vehicles to recognize traffic signs in low-light conditions. Nighttime object detection has numerous problems and is not less difficult than daytime detection. This research employs the YOLOv9 algorithm, a state-of-the-art, one-stage object detection model known for its speed and accuracy in identifying traffic signs during nighttime. The Contrast Limited Adaptive Histogram Equalization (CLAHE) method is evaluated and compared with nocturnal road sign detection. This study integrates YOLOv9 and CLAHE to provide an ideal model for enhancing nighttime road sign recognition efficiency. Our results indicate that the combination of YOLOv9 and CLAHE achieves the highest mean Average Precision (mAP) of 76.2%. The suggested model exhibits potential for incorporation into autonomous vehicle systems, facilitating real-time identification of road objects, pedestrians, and other vehicles, hence enhancing safety and navigation. © 2025 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license |
| Author Keywords | autonomous vehicles; Contrast Limited Adaptive Histogram Equalization; Convolutional Neural Network; deep learning; nighttime detection; YOLOv9 |
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