Vehicle identification using modified region based convolution network for intelligent transportation system

Sharma, Poonam; Singh, Akansha; Singh, Krishna Kant; Dhull, Anuradha

doi:10.1007/s11042-020-10366-x

Cited by 23 publications

(11 citation statements)

References 30 publications

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“…When studying the distance metric pattern recognition matching algorithm, pattern matching can be performed only after completing the feature extraction and parameterization of the detected waveform of the locomotive under test [8]. The distance metric algorithm is described as follows: when applying the distance metric algorithm for locomotive fault detection, the distance ) ,..., , (…”

Section: Pattern Matching Locomotive Fault Detection Algorithmmentioning

confidence: 99%

Research and application of intelligent traffic system on locomotive overhaul process

He¹

2023

International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022)

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With the rapid development of information technology and the transformation of manufacturing services, the overhaul activities of complex equipment are receiving more and more attention. Locomotives, as an important part of the rail transportation industry, are typical complex technical equipment with a long-life cycle and require multiple overhaul work during the life cycle to ensure safe operation. At the same time, because of the complex structure of locomotive products, high operational reliability requirements, large downtime losses, and many levels of maintenance, simple maintenance processes are not applicable. The Intelligent Transportation System (ITS) can monitor and detect the traffic information parameters in real time, so the ITS is used to troubleshoot locomotives and enable them to operate safely. It can be seen from this study that the application of intelligent transportation system in Locomotive Maintenance (LM) can be used as locomotive maintenance engine and monitor the maintenance process to keep the locomotive running well.

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Section: Pattern Matching Locomotive Fault Detection Algorithmmentioning

confidence: 99%

Research and application of intelligent traffic system on locomotive overhaul process

He¹

2023

International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022)

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“…The method not only reduced the number of operations and saved running time, but also improved the detection results, as shown in Figure 2. Then, gray scale images synthesized the information of each red-green-blue color mode (RGB) chan nel of the true color bitmap, which could make image processing more convenient and efficient [17]. Therefore, the color image was converted to a grayscale image based on th established video maturity analysis algorithm, as in Figure 3, where the rectangula wireframe range is ROI.…”

Section: Canny Edge Detectionmentioning

confidence: 99%

Vehicle Recognition from Unmanned Aerial Vehicle Videos Based on Fusion of Target Pre-Detection and Deep Learning

et al. 2022

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For accurate and effective automatic vehicle identification, morphological detection and deep convolutional networks were combined to propose a method for locating and identifying vehicle models from unmanned aerial vehicle (UAV) videos. First, the region of interest of the video frame image was sketched and grey-scale processing was performed; sub-pixel-level skeleton images were generated based on the Canny edge detection results of the region of interest; then, the image skeletons were decomposed and reconstructed. Second, a combination of morphological operations and connected domain morphological features were applied for vehicle target recognition, and a deep learning image benchmark library containing 244,520 UAV video vehicle samples was constructed. Third, we improved the AlexNet model by adding convolutional layers, pooling layers, and adjusting network parameters, which we named AlexNet*. Finally, a vehicle recognition method was established based on a candidate target extraction algorithm with AlexNet*. The validation analysis revealed that AlexNet* achieved a mean F1 of 85.51% for image classification, outperforming AlexNet (82.54%), LeNet (63.88%), CaffeNet (46.64%), VGG16 (16.67%), and GoogLeNet (14.38%). The mean values of Pcor, Pre, and Pmiss for cars and buses reached 94.63%, 6.87%, and 4.40%, respectively, proving that this method can effectively identify UAV video targets.

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“…Kumari et al examined the application of a pretrained CNN in forensics for offline signature detection [ 21 ], while Shibli et al investigated the implementation of pretrained CNN for artificial intelligent drone-based encrypted machine learning of image extraction [ 22 ]. In 2021, Rajadurai et al examined the detection of cracks in concrete surfaces through deep learning vision using AlexNet CNN [ 23 ], and Sharma et al evaluated the identification of vehicles using region-based CNN with an intelligent transportation focus [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Tire Condition Monitoring Using Transfer Learning-Based Deep Neural Network Approach

Vasan

Sridharan

Sreelatha

et al. 2023

Sensors

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Monitoring tire condition plays a deterministic role in the overall safety and economy of an automobile. The tire condition monitoring system (TCMS) alerts the driver of the vehicle if the inflation pressure of a particular tire decreases below a specific value. Owing to the high costs involved in realizing this system, most vehicles do not feature this technology as a standard. With highly robust and accurate sensors making their way into an increasing number of applications, obtaining signals of varied types (especially vibration signals) is becoming easier and more modularized. In addition, feature-based machine learning techniques that enable accurate responses to varied input conditions have sought greater scientific attention. However, deep learning is gradually finding greater applications pertaining to condition monitoring. One approach of deep learning is presented in this paper, which instantaneously monitors the vehicle tire condition. For this purpose, vibration signals were obtained through the rotation of the tire under different inflation pressure conditions using a low-cost microelectromechanical system (MEMS) accelerometer.

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Vehicle identification using modified region based convolution network for intelligent transportation system

Cited by 23 publications

References 30 publications

Research and application of intelligent traffic system on locomotive overhaul process

Research and application of intelligent traffic system on locomotive overhaul process

Vehicle Recognition from Unmanned Aerial Vehicle Videos Based on Fusion of Target Pre-Detection and Deep Learning

Tire Condition Monitoring Using Transfer Learning-Based Deep Neural Network Approach

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