Traffic Congession Detection through Modified Resnet50 and Prediction of Traffic using Clustering

Prawinsankar, D; Gunasekaran, M.; Gopalakrishnan, B; Purusothaman, P.

doi:10.1109/stcr51658.2021.9588999

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…The first stage of the detection network adopts the Resnet50 [27,28] network (shown in figure 4). The network structure is divided into six major parts: input modules, four blocks (the four stages shown in figure 4), and output modules.…”

Section: The First-level Detection Networkmentioning

confidence: 99%

Super-resolution reconstruction of ultrasonic Lamb wave TFM image via deep learning

Zhang¹,

chai²,

Zhu³

et al. 2023

Meas. Sci. Technol.

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Under the same detection frequency and depth, when the center spacing of multiple defects is less than the resolution threshold determined by the Rayleigh criterion, it is challenging to achieve super-resolution imaging of multiple defects using the ultrasonic Total Focusing Method (TFM). A multilevel deep learning network is proposed as a super-resolution reconstruction method for ultrasonic Lamb wave TFM images . The first-level network is a detection network that uses a Resnet50 with more convolutional layers to improve the linear expression capabilities of the neural network. It introduces residual structure to solve low accuracy issues of multi-convolutional layers so that the Resnet50 can accurately detect defects from TFM images. The second-level network is a super-resolution reconstruction network that uses a Deeplab v3+ with a dilated convolutional layer. It controls the receptive field without changing the feature map size of the TFM image. With this model, a super-resolution reconstruction of multiple defects with a center spacing less than the resolution threshold is realized by extracting the detailed features of defects in the TFM image. Experimental results show that when the defect center spacing is greater than the resolution threshold determined by the Rayleigh criterion, the super-resolution reconstruction method improves the calculation accuracy of the defect center spacing by 4.7% and the calculation accuracy of the defect area by 93.7% compared with TFM. When the defect spacing is less than the resolution threshold, the method can still identify and accurately calculate the center spacing of multiple defects.

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Section: The First-level Detection Networkmentioning

confidence: 99%

Super-resolution reconstruction of ultrasonic Lamb wave TFM image via deep learning

Zhang¹,

chai²,

Zhu³

et al. 2023

Meas. Sci. Technol.

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Pedestrian Crossing Detection by VGG16 for Visually-impaired Walking Assistance System

Yoshikawa

Premachandra

2022

2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI)

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A New Multi-Branch Convolutional Neural Network and Feature Map Extraction Method for Traffic Congestion Detection

Jiang,

Feng,

Zhang

et al. 2024

Sensors

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With the continuous advancement of the economy and technology, the number of cars continues to increase, and the traffic congestion problem on some key roads is becoming increasingly serious. This paper proposes a new vehicle information feature map (VIFM) method and a multi-branch convolutional neural network (MBCNN) model and applies it to the problem of traffic congestion detection based on camera image data. The aim of this study is to build a deep learning model with traffic images as input and congestion detection results as output. It aims to provide a new method for automatic detection of traffic congestion. The deep learning-based method in this article can effectively utilize the existing massive camera network in the transportation system without requiring too much investment in hardware. This study first uses an object detection model to identify vehicles in images. Then, a method for extracting a VIFM is proposed. Finally, a traffic congestion detection model based on MBCNN is constructed. This paper verifies the application effect of this method in the Chinese City Traffic Image Database (CCTRIB). Compared to other convolutional neural networks, other deep learning models, and baseline models, the method proposed in this paper yields superior results. The method in this article obtained an F1 score of 98.61% and an accuracy of 98.62%. Experimental results show that this method effectively solves the problem of traffic congestion detection and provides a powerful tool for traffic management.

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Traffic Congession Detection through Modified Resnet50 and Prediction of Traffic using Clustering

Cited by 3 publications

References 7 publications

Super-resolution reconstruction of ultrasonic Lamb wave TFM image via deep learning

Super-resolution reconstruction of ultrasonic Lamb wave TFM image via deep learning

Pedestrian Crossing Detection by VGG16 for Visually-impaired Walking Assistance System

A New Multi-Branch Convolutional Neural Network and Feature Map Extraction Method for Traffic Congestion Detection

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