Intelligent Traffic-Monitoring System Based on YOLO and Convolutional Fuzzy Neural Networks

Lin, Cheng‐Jian; Jhang, Jyun-Yu

doi:10.1109/access.2022.3147866

Cited by 45 publications

(20 citation statements)

References 45 publications

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“…Similarly, nighttime vehicle tracking is a challenge that is not addressed in this study. In [22], Cheng-Jian Lin et al introduced a three-tier system that is proficient in detecting, counting, and classification of vehicles in different scenarios.…”

Section: B Deep Learning-based Vehicle Detectionmentioning

confidence: 99%

Smart Traffic Monitoring Through Pyramid Pooling Vehicle Detection and Filter-Based Tracking on Aerial Images

et al. 2023

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Increased traffic density, combined with global population development, has resulted in increasingly congested roads, increased air pollution, and increased accidents. Globally, the overall number of automobiles has expanded dramatically during the last decade. Traffic monitoring in this environment is undoubtedly a significant difficulty in various developing countries. This work introduced a novel vehicle detection and classification system for smart traffic monitoring that uses a convolutional neural network (CNN) to segment aerial imagery. These segmented images are examined to further detect the vehicles by incorporating novel customized pyramid pooling. Then, these detected vehicles are classified into various subcategories. Finally, these vehicles are tracked via Kalman filter (KF) and kernelized filter-based techniques to cope with and manage massive traffic flows with minimal human intervention. During the experimental evaluation, our proposed system illustrated a remarkable vehicle detection rate of 95.78% over the Vehicle Aerial Imagery from a Drone (VAID), 95.18% over the Vehicle Detection in Aerial Imagery (VEDAI), and 93.13% over the German Aerospace Center (DLR) DLR3K datasets, respectively. The proposed system has a variety of applications, including identifying vehicles in traffic, sensing traffic congestion on a road, traffic density at intersections, detecting various types of vehicles, and providing a path for pedestrians.

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Section: B Deep Learning-based Vehicle Detectionmentioning

confidence: 99%

Smart Traffic Monitoring Through Pyramid Pooling Vehicle Detection and Filter-Based Tracking on Aerial Images

et al. 2023

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“…A method for traffic monitoring based on YOLO (You Only Look Once) and CFNN (Convolutional Fuzzy Neural Network) was presented by [Lin and Jhang, (2022)]. The suggested system operated in three phases: detection, counting, and classification of vehicles.…”

Section: Related Workmentioning

confidence: 99%

“…During the classification process, vehicles are classified into numerous groups. Previously, the categorization was carried out using the Faster RCNN [Huy and Duc, (2020)] model, Convolutional Fuzzy Neural Network (CFNN) [Lin and Jhang, (2022)], and various machine learning-based algorithms [Azimjonov and Ozmens, (2021)], etc. At this stage, the identified cars were classified into three categories: high, medium, and low traffic density.…”

Section: Vehicle Density Classificationmentioning

confidence: 99%

Traffic Monitoring System for Smart City Based on Traffic Density Estimation

Bidwe¹,

Kale²,

Bidwe³

2022

INDJCSE

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Nowadays, traffic monitoring systems are at the frontline of smart city movement, and traffic density estimation is useful to a traffic monitoring system. The system of this work estimates traffic density using a five-layered CNN with a variety of input feature maps and filter sizes. There are 64, 64, 96, 96, and 96 feature maps for each pair of convolutions and max-pooling layers, and each pair's corresponding filter sizes are 5*5, 3*3, 5*5, 3*3, and 3*3. The proposed system divides the traffic into three categories: High, Medium, and Low traffic based on images that are taken from traffic videos that are recorded by traffic surveillance cameras. To test the system, we used the WSDT (Washington State Department of Traffic Transportation) Dataset of recorded video footage from the highway CCTVs in cities Seattle and Washington. The model is evaluated using parameters such as 0.99 precision, 0.99 recall, 0.99 f1-score, and model accuracy of 99.6 %. By examining the dataset, we have trained the model in such a manner to produce better results.

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“…You Only Look Once or YOLO is Intelligent neural network for real-time detection. YOLO applies a single neural network to the whole picture (Lin & Jhang, 2022;Dai et al, 2022). This network will be shared into regions, then will predict bounding boxes and probabilities.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of Object Detection With You Only Look Once Algorithm in Limited Face-To-Face Times in Pandemic

Yunefri

Agushybana

Fadrial

et al. 2022

JAETS

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Covid-19 has hit many countries in the world, including Indonesia. The rapid and deadly spread of Covid-19 reached Indonesia in early 2020. This pandemic of course had a detrimental impact on the Indonesian people in terms of health, economy, education and others. The Indonesian government certainly does not remain silent, the government is aggressively making efforts to break the Covid-19 chain in various ways, one of the efforts made is to continuously inform about the health protocols recommended by the government to prevent transmission. The Indonesian Ministry of Health (in Mardhia et al., 2020) Efforts that can be made in the prevention phase by each individual include: Wearing a mask, Wearing gloves, Using hand sanitizer/disinfectant, Washing hands with soap, Avoiding touching the face, Avoid shaking hands, Avoid gatherings or long queues, Avoid touching objects/object surfaces in public areas, Avoid taking public transportation, Maintain a distance of at least two meters from other people when outside the house, and If you show symptoms of illness, immediately notify the people around. Even though they have been informed about the prevention of Covid-19, the public tends to be negligent in implementing health protocols, one of which is the application of Social Distancing. Therefore, this study will create a distance detector using the YOLOv3 algorithm as one of the detection objects for the implementation of community activity restrictions

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Intelligent Traffic-Monitoring System Based on YOLO and Convolutional Fuzzy Neural Networks

Cited by 45 publications

References 45 publications

Smart Traffic Monitoring Through Pyramid Pooling Vehicle Detection and Filter-Based Tracking on Aerial Images

Smart Traffic Monitoring Through Pyramid Pooling Vehicle Detection and Filter-Based Tracking on Aerial Images

Traffic Monitoring System for Smart City Based on Traffic Density Estimation

Implementation of Object Detection With You Only Look Once Algorithm in Limited Face-To-Face Times in Pandemic

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