A vehicle detection scheme based on two-dimensional HOG features in the DFT and DCT domains

Naiel, Mohamed A.; Ahmad, M. Omair; Swamy, M.N.S.

doi:10.1007/s11045-018-0621-1

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…Since the traditional HOG method can only calculate the gradient features in both horizontal and vertical directions, in [ 144 ], Compass-HOG was designed to expand the direction dimension of image gradient calculation to reduce information loss and improve accuracy. In [ 145 ], 2D-HOG is designed to deal with the problem of resolution change of input image, and the accuracy was also improved compared with HOG.…”

Section: Vehicle Detection: Vision-based Methodsmentioning

confidence: 99%

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Liu

Yuan

et al. 2021

Sensors

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Unmanned ground vehicles (UGVs) have great potential in the application of both civilian and military fields, and have become the focus of research in many countries. Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance. This article firstly introduces commonly used sensors for vehicle detection, lists their application scenarios and compares the strengths and weakness of different sensors. Secondly, related works about one of the most important aspects of environmental perception technology—vehicle detection—are reviewed and compared in detail in terms of different sensors. Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms. In addition, some datasets about UGVs are summarized to achieve the verification of vehicle detection algorithms in practical application. Finally, promising research topics in the future study of vehicle detection technology for UGVs are discussed in detail.

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Section: Vehicle Detection: Vision-based Methodsmentioning

confidence: 99%

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Liu

Yuan

et al. 2021

Sensors

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“…Template matching [6,7] is a common traditional location method. In the aspect of image feature extraction, there are local binary pattern feature [8], Histogram of Oriented Gradient (HOG) [9,10], Haar feature [11], and other features. After obtaining image features, the similarity measure [12,13] is used to classify and locate the target.…”

Section: Introductionmentioning

confidence: 99%

Positioning of the Moving and Static Contacts of the Switch Machine Based on Double-Layer Mask R-CNN

Yin

Chen

et al. 2021

Complexity

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With the continuous development of rail transit, the maintenance of the switch machine becomes more and more important, and the contact depth of the moving contact and static contact in the switch machine is a key part of it. At present, the manual measurement method is the main measure of contact depth, which has the problems of low efficiency and strong subjectivity. The measurement of contact depth based on machine vision includes two steps: moving and static contact positioning and distance conversion. The positioning result will have an important impact on distance measurement. Therefore, a positioning method for moving and static contact based on double-layer Mask R-CNN (DLM) is proposed in this paper: first, the moving contact is roughly positioned by Mask R-CNN to obtain the predicted target area; second, the subgraph of the target area is preprocessed; finally, the precise positioning is used to determine the precise position of the moving and static contact. The accuracy and robustness of the proposed DLM are verified by the internal image of the switch machine.

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“…Machine vision-based track fastener defect detection methods are mainly divided into shallow 10 and deep learning methods. 11,12 Shallow learning methods mainly extract ''artificially designed'' image features, which mainly include local binary pattern features, 13 image gradient histogram features, 14,15 image Haar features, 16,17 image edge features, 18 and scale invariant feature transform image features. 19 The extracted features combined with AdaBoost classifier, 20,21 k-nearest neighbor classifier, 22 linear classifier, 23 support vector machine, [24][25][26] and other classifiers for image classification and detection.…”

Section: Introductionmentioning

confidence: 99%

Pandrol track fastener defect detection based on local convolutional neural networks

Chen

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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The Pandrol track fastener image is composed of two parts: track fastener clip sub-graph and track fastener bolt sub-graph. However, the detection of track fastener clip defect can be realized by track fastener image and track fastener image cannot effectively detect whether the bolt is loose. When the convolutional neural network is used to extract whole picture features and detect, many bolt features unrelated to the clips will be obtained, thereby resulting in a high false alarm rate. To solve these problems, a method based on local convolutional neural network to detect the Pandrol track fastener defects is proposed. First, the algorithm for automatic segmentation of track fastener pictures was used to divide the picture of the Pandrol track fastener into two sub-pictures, one sub-picture is the track fastener bolt and the other sub-picture is the track fastener clip. Second, convolutional neural network was used to detect the track fastener clip pictures. The influence of bolt features unrelated to clips on clips detection can be avoided through image segmentation for local feature extraction, thereby reducing the false alarm rate. Finally, the validity of the proposed method is verified using real Pandrol track fastener images.

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A vehicle detection scheme based on two-dimensional HOG features in the DFT and DCT domains

Cited by 5 publications

References 39 publications

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Positioning of the Moving and Static Contacts of the Switch Machine Based on Double-Layer Mask R-CNN

Pandrol track fastener defect detection based on local convolutional neural networks

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