An Improved Intelligent Transportation Algorithm Based on Image Processing

Su, Yang; Guo, Zhenzi; Zhang, Qi

doi:10.1109/isms.2018.00014

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…With the development of machine vision technology, computer vision technology is widely applied in the fields such as security [10,11], agricultural production [12,13], and intelligent transportation [14]. Therefore, it has also become the mainstream direction of longitudinal tear detection [15].…”

Section: Introductionmentioning

confidence: 99%

Sound-Based Improved DenseNet Conveyor Belt Longitudinal Tear Detection

Miao

Wang

2022

IEEE Access

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At present, conveyor belt tearing is the most serious fault of belt conveyor, causing the greatest loss. This paper aims to overcome the issues of poor accuracy, low precision, and poor real-time performance in the longitudinal tear detection of the conveyor belt of the belt conveyor. Specifically, this paper presents a method of longitudinal tear detection of conveyor belt based on the sound signal. According to the recognition of the tearing sound signal, the longitudinal tearing of conveyor belt can be detected. A dynamic MFCC feature extraction method is proposed to extract the sound signal feature. An improved DenseNet neural network model is designed, which is used to classify the longitudinal sound of the belt conveyor to realize the longitudinal tearing detection of the conveyor belt.The experimental results demonstrate that the method in this paper achieves the sound detection of the longitudinal tear of the conveyor belt, and the average accuracy of the longitudinal tear detection of the conveyor belt of the belt conveyor reaches 95.42%, which satisfies the requirements of the longitudinal tear detection of the conveyor belt of the belt conveyor.Applying this method to the longitudinal tearing detection of conveyor belt can solve the shortcomings of existing methods and realize the detection of the longitudinal tearing fault of conveyor belt.

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Section: Introductionmentioning

confidence: 99%

Sound-Based Improved DenseNet Conveyor Belt Longitudinal Tear Detection

Miao

Wang

2022

IEEE Access

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“…Radar is an important part of the contemporary intelligent transportation system [1][2][3]. Multi-target tracking with radar is also a hot issue in intelligent transportation research [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Density Peak Fuzzy Clustering Algorithm for Moving Vehicles Using Traffic Radar

et al. 2019

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The detection of adjacent vehicles in highway scenes has the problem of inaccurate clustering results. In order to solve this problem, this paper proposes a new clustering algorithm, namely Spindle-based Density Peak Fuzzy Clustering (SDPFC) algorithm. Its main feature is to use the density peak clustering algorithm to perform initial clustering to obtain the number of clusters and the cluster center of each cluster. The final clustering result is obtained by a fuzzy clustering algorithm based on the spindle update. The experimental data are the radar echo signal collected in the real highway scenes. Compared with the DBSCAN, FCM, and K-Means algorithms, the algorithm has higher clustering accuracy in certain scenes. The average clustering accuracy of SDPFC can reach more than 95%. It is also proved that the proposed algorithm has strong robustness in certain highway scenes.

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An Improved Intelligent Transportation Algorithm Based on Image Processing

Cited by 2 publications

References 4 publications

Sound-Based Improved DenseNet Conveyor Belt Longitudinal Tear Detection

Sound-Based Improved DenseNet Conveyor Belt Longitudinal Tear Detection

A Novel Density Peak Fuzzy Clustering Algorithm for Moving Vehicles Using Traffic Radar

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