The Real-Time Detection of Traffic Participants Using YOLO Algorithm

Corovic, Aleksa; Ilic, Velibor; Duric, Sinica; Marijan, Mališa; Pavkovic, Bogdan

doi:10.1109/telfor.2018.8611986

Cited by 100 publications

(37 citation statements)

References 5 publications

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“…In their conclusion, the model could detect an object and overlayed 3D graphics at the location of an object in an effective way. [39] YOLOv3 algorithm was used to detect the five classes of a real-time object of traffic participants or road signalization in advanced driver assistance systems (ADAS). The proposed system evaluated on NVidia GeForce GTX 1060GPU by using the weights on the COCO pre-trained model and trained on the Berkley deep drive dataset.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

An assistive model of obstacle detection based on deep learning: YOLOv3 for visually impaired people

Rachburee

Punlumjeak

2021

IJECE

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The World Health Organization (WHO) reported in 2019 that at least 2.2 billion people were visual-impairment or blindness. The main problem of living for visually impaired people have been facing difficulties in moving even indoor or outdoor situations. Therefore, their lives are not safe and harmful. In this paper, we proposed an assistive application model based on deep learning: YOLOv3 with a Darknet-53 base network for visually impaired people on a smartphone. The Pascal VOC2007 and Pascal VOC2012 were used for the training set and used Pascal VOC2007 test set for validation. The assistive model was installed on a smartphone with an eSpeak synthesizer which generates the audio output to the user. The experimental result showed a high speed and also high detection accuracy. The proposed application with the help of technology will be an effective way to assist visually impaired people to interact with the surrounding environment in their daily life.

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Section: Literature Review and Related Workmentioning

confidence: 99%

An assistive model of obstacle detection based on deep learning: YOLOv3 for visually impaired people

Rachburee

Punlumjeak

2021

IJECE

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“…Algorithms and ANNs such as YOLO not only solve this problem but also have no loss of accuracy. Ćorović et al (8) pointed out that object detection is one of the important functions of software that will provide the next generation of automobiles with automatic driving. In their research, they trained a neural network on five object classes (cars, trucks, pedestrians, traffic signs, and lights), and the method was shown to be effective in a variety of driving conditions (sunny, cloudy, snow, foggy, and night).…”

Section: Research On Application Of Yolo Algorithmmentioning

confidence: 99%

“…3. (Color online) Identification of vehicles and traffic signs under different weather conditions (8).…”

mentioning

confidence: 99%

Identifying Vehicles Dynamically on Freeway CCTV Images through the YOLO Deep Learning Model

Pan¹,

Wang²

2021

Sensors and Materials

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This study focuses on object detection in computer vision research. The object detection process often encounters many uncertainties, such as the uncertainty of the number of objects in the image, the different conditions of the objects including their appearance, the current driving speed, the obstruction between vehicles, sunlight in the daytime, the lack of light at night, the irreversible factors related to the CCTV lens, and other factors, which make object detection and image preprocessing difficult. Taiwan's freeways are all equipped with CCTV to monitor realtime road conditions, and all CCTV images are available to the public via the internet. However, in freeway segments and tunnels, and even on traffic-prone roads, traffic jams and accidents are only judged by "human power." Therefore, in this study, we use existing CCTV streaming video as a vehicle sensor data source and the You Only Look Once (YOLO) algorithm to perform object detection as well to tune adjustable parameters to achieve the desired results. From the preliminary results of this study, the current model based on the YOLOv3 algorithm and the Common Objects in Context (COCO) image dataset has an accuracy of 44% during the daytime and 41% during the nighttime for CCTV cameras installed outdoors. In the future, we will analyze larger amounts of CCTV video streaming data to detect whether a road is congested and even detect the occurrence of traffic accidents.

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“…Some researchers [26,27] have extracted information from the self-driving environment to assist self-driving based on the 2-stage detection algorithm or unified detection algorithm. Ref.…”

Section: Image Recognition Algorithms For Self-driving Object Detectionmentioning

confidence: 99%

“…Ref. [27] claimed that it is difficult to use the 2-stage detection algorithm in a real-time detection environment due to the slow response time, and thus, object detection is performed using the unified detection algorithm, YOLO, to improve the speed. The YOLO V3 algorithm is used to detect traffic participants, including cars, trucks, pedestrians, traffic signs, and traffic lights.…”

Section: Image Recognition Algorithms For Self-driving Object Detectionmentioning

confidence: 99%

A Review of Intelligent Self-Driving Vehicle Software Research

2019

KSII TIIS

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Interest in self-driving vehicle research has been rapidly increasing, and related research has been continuously conducted. In such a fast-paced self-driving vehicle research area, the development of advanced technology for better convenience safety, and efficiency in road and transportation systems is expected. Here, we investigate research in self-driving vehicles and analyze the main technologies of driverless car software, including: technical aspects of autonomous vehicles, traffic infrastructure and its communications, research techniques with vision recognition, deep leaning algorithms, localization methods, existing problems, and future development directions. First, we introduce intelligent self-driving car and road infrastructure algorithms such as machine learning, image processing methods, and localizations. Second, we examine the intelligent technologies used in self-driving car projects, autonomous vehicles equipped with multiple sensors, and interactions with transport infrastructure. Finally, we highlight the future direction and challenges of self-driving vehicle transportation systems.

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The Real-Time Detection of Traffic Participants Using YOLO Algorithm

Cited by 100 publications

References 5 publications

An assistive model of obstacle detection based on deep learning: YOLOv3 for visually impaired people

An assistive model of obstacle detection based on deep learning: YOLOv3 for visually impaired people

Identifying Vehicles Dynamically on Freeway CCTV Images through the YOLO Deep Learning Model

A Review of Intelligent Self-Driving Vehicle Software Research

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