Research on the Method of Determining Highway Truck Load Limit Based on Image Processing

Mo, Xianglun; Sun, Chuanpeng; Li, Dongda; Huang, Shuang; Hu, Tianci

doi:10.1109/access.2020.3037195

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Then, it utilizes this structural graph as guidance to generate effective vehicle representations. Mo et al (2020) analyzed the relationship between the number and distribution of vehicle axles and the weight limit of trucks. They proposed a circular detection method based on an improved Hough and clustering algorithm to identify the axles of trucks.…”

Section: Literature Reviewmentioning

confidence: 99%

Truck model recognition for an automatic overload detection system based on the improved MMAL-Net

Sun,

Su,

Yan

et al. 2023

Front. Neurosci.

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Efficient and reliable transportation of goods through trucks is crucial for road logistics. However, the overloading of trucks poses serious challenges to road infrastructure and traffic safety. Detecting and preventing truck overloading is of utmost importance for maintaining road conditions and ensuring the safety of both road users and goods transported. This paper introduces a novel method for detecting truck overloading. The method utilizes the improved MMAL-Net for truck model recognition. Vehicle identification involves using frontal and side truck images, while APPM is applied for local segmentation of the side image to recognize individual parts. The proposed method analyzes the captured images to precisely identify the models of trucks passing through automatic weighing stations on the highway. The improved MMAL-Net achieved an accuracy of 95.03% on the competitive benchmark dataset, Stanford Cars, demonstrating its superiority over other established methods. Furthermore, our method also demonstrated outstanding performance on a small-scale dataset. In our experimental evaluation, our method achieved a recognition accuracy of 85% when the training set consisted of 20 sets of photos, and it reached 100% as the training set gradually increased to 50 sets of samples. Through the integration of this recognition system with weight data obtained from weighing stations and license plates information, the method enables real-time assessment of truck overloading. The implementation of the proposed method is of vital importance for multiple aspects related to road traffic safety.

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Section: Literature Reviewmentioning

confidence: 99%

Truck model recognition for an automatic overload detection system based on the improved MMAL-Net

Sun,

Su,

Yan

et al. 2023

Front. Neurosci.

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“…Change power value (γ) can change the gamma curve of CRT. Experienced, γ =1 is the calculated boundary value, and c is 0.5 [26].…”

Section: Gamma Correctingmentioning

confidence: 99%

Research on Expressway Traffic Event Detection at Night Based on Mask-SpyNet

Sun

Zhang

et al. 2022

IEEE Access

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Expressway traffic event detection at night is essential for improving rescue efficiency and avoiding secondary accidents. Most expressways in China have built a complete Expressway video monitoring system. However, at night, the expressway traffic event detection still adopts manual detection, which is inefficient. In this dissertation, the strategy of expressway traffic event detection at night has been analysed first. On this basis, by combining the Mask method and SpyNet deep learning, this study develops a night highway vehicle detection deep learning network with a dense optical flow formed by night vehicle light flow as the detection object. Finally, the Deepsort algorithm is used to track and measure the velocity of the detected target. The measured data are used to compare the background difference method, classical optical flow method, YOLO-v3 and proposed method in this paper. The results show that the proposed method has the advantages of high detection accuracy and fast detection speed.INDEX TERMS Expressway traffic event detection at night, Image processing, Mask algorithm, SpyNet.

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A Convolutional Neural Network Model for Identifying Unclassified and Misclassified Vehicles Using Spatial Pyramid Pooling

Bao

Jiang

et al. 2022

Construction Research Congress 2022

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Research on the Method of Determining Highway Truck Load Limit Based on Image Processing

Cited by 5 publications

References 23 publications

Truck model recognition for an automatic overload detection system based on the improved MMAL-Net

Truck model recognition for an automatic overload detection system based on the improved MMAL-Net

Research on Expressway Traffic Event Detection at Night Based on Mask-SpyNet

A Convolutional Neural Network Model for Identifying Unclassified and Misclassified Vehicles Using Spatial Pyramid Pooling

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