A YOLO Based Approach for Traffic Light Recognition for ADAS Systems

Mostafa, Miand; Ghantous, Milad

doi:10.1109/miucc55081.2022.9781682

Cited by 8 publications

(2 citation statements)

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“…In contrast, YOLO surpasses SSD in terms of accuracy. 54 Hence, YOLO stands out by delivering a well-rounded performance in both accuracy and operational speed. In this study, the YOLOv8s network is employed for traffic light color recognition.…”

Section: Traffic Light Recognition Using Yolov8mentioning

confidence: 99%

Traffic light recognition and V2I communications of an autonomous vehicle with the traffic light for effective intersection navigation using YOLOv8 and MAVS simulation

Rahman,

Islam,

Ball

et al. 2024

Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2024

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We integrate advanced computer vision and Vehicle-to-Infrastructure (V2I) communication systems for effective intersection navigation. In the first phase, the YOLOv8 deep learning model is employed to accurately detect traffic lights, with specialized training on the S2TLD Dataset for precision. Then we establish seamless V2I communication in MAVS Simulation, allowing vehicles to receive Signal Phase and Timing (SPaT) messages from traffic lights, enabling autonomous adjustment of speed and behavior. Simulating the scenarios in a high-fidelity automotive simulator demonstrates accurate traffic light detection and timely phase information, promising safer and more efficient intersection navigation for autonomous vehicles.

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Section: Traffic Light Recognition Using Yolov8mentioning

confidence: 99%

Traffic light recognition and V2I communications of an autonomous vehicle with the traffic light for effective intersection navigation using YOLOv8 and MAVS simulation

Rahman,

Islam,

Ball

et al. 2024

Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2024

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“…Furthermore, due to the influence of epidemics, there were many mask identification studies based on the use of different algorithms [ 19 , 20 , 21 , 22 ]. There has been a lot of interest in self-driving cars such as automatic navigation [ 23 ], driver’s assistance [ 24 ], vehicle detection [ 25 ], vehicle tracking [ 26 ], and blind-spot detection [ 27 ]. The use of deep learning for target identification improves the accuracy of identification and also accelerates the speed of identifying targets.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Clothing Style Recognition Using YOLOv5

Chang

Zhang

2022

Micromachines

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With the rapid development of artificial intelligence, much more attention has been paid to deep learning. However, as the complexity of learning algorithms increases, the needs of computation power of hardware facilities become more crucial. Instead of the focus being on computing devices like GPU computers, a lightweight learning algorithm could be the answer for this problem. Cross-domain applications of deep learning have attracted great interest amongst researchers in academia and industries. For beginners who do not have enough support with software and hardware, an open-source development environment is very helpful. In this paper, a relatively lightweight algorithm YOLOv5s is addressed, and the Google Colab is used for model training and testing. Based on the developed environment, many state-of-art learning algorithms can be studied for performance comparisons. To highlight the benefits of one-stage object detection algorithms, the recognition of clothing styles is investigated. The image samples are selected from datasets of fashion clothes and the web crawling of online stores. The image data are categorized into five groups: plaid; plain; block; horizontal; and vertical. Average precison, mean average precison, recall, F1-score, model size, and frame per second are the metrics used for performance validations. From the experimental outcomes, it shows that YOLOv5s is better than other learning algorithms in the recognition accuracy and detection speed.

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