Modern Methods of Image Processing in Safety-Critical Applications within Intelligent Transportation System

Automatic License Plate Recognition is related to the Intelligent Transportation System (ITS) that supports the road's e-law enforcement system. In the case of the Indonesian license plate, with various colour rules for font and background, and sometimes vehicle owners modify their license plate font format, this is a challenge in the image processing approach. This research utilizes pre-trained of AlexNet, VGGNet, and ResNet to determine the optimum model of Indonesian character license plate recognition. Three pre-trained approaches in CNN-based detection for reducing time for a build if model from scratch. The experiment shows that using the pre-trained ResNet model gives a better result than another two approaches. The optimum results were obtained at epoch 50 with an accuracy of 99.9% and computation time of 26 minutes. This experiment results fulfil the goal of this research. Keywords : ALPR; ITS; CNN; AlexNet; VGGNet; ResNet

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Vehicle Classification and Counting System Using YOLO Object Detection Technology

Traitement du signal ◽

10.18280/ts.380419 ◽

2021 ◽

Vol 38 (4) ◽

pp. 1087-1093

Author(s):

Jian-Da Wu ◽

Bo-Yuan Chen ◽

Wen-Jye Shyr ◽

Fan-Yu Shih

Keyword(s):

Image Processing ◽

Object Detection ◽

Intelligent Transportation System ◽

Confusion Matrix ◽

Intelligent Transportation ◽

Traffic Monitoring ◽

Transportation System ◽

Essential Information ◽

Flow Monitoring ◽

Detection Technology

The intelligent transportation system is one of the most important constructions of urban modernization. Traffic flow monitoring technology is the most essential information in the intelligent transportation system. With the advancements in instrumentation, computer image processing and communication technology, computerized traffic monitoring technologies have become feasible. This study captures traffic information using surveillance cameras installed at higher locations. The YOLO object detection technology is used to identify vehicle types. The system principle uses image processing and deep convolutional neural networks for object detection training. Vehicle type identification and counting are carried out in this study for straight-line bidirectional roads, and T-shaped and cross-type intersections. A counting line is defined in the vehicle path direction using the object tracking method. The center coordinate of the object moves through the counting line. The number of motorcycles, small vehicles, and large vehicles were counted in different road sections. The actual number of vehicles on the road was compared with the number of vehicles measured by the system. Three separate counting periods were used to define the results using the confusion matrix.

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Kajian Literatur Metode Pendeteksian dan Pengenalan Kendaraan Berdasarkan Citra Digital

Petir ◽

10.33322/petir.v13i2.1026 ◽

2020 ◽

Vol 13 (2) ◽

pp. 223-228

Author(s):

Septia Rani ◽

Aldhiyatika Amwin

Keyword(s):

Image Processing ◽

Real Time ◽

Intelligent Transportation System ◽

Intelligent Transportation ◽

Transportation System

Pendeteksian dan pengenalan kendaraan menjadi topik yang menarik oleh para peneliti terutama di bidang visi komputer. Sistem pendeteksian dan pengenalan kendaraan secara otomatis dan real-time merupakan bagian penting pada Intelligent Transportation System (ITS). Pada makalah ini membahas beberapa kajian literatur tentang metode yang digunakan untuk pendeteksian dan pengenalan kendaraan. Kajian dilakukan dengan cara meninjau literatur yang berhubungan dengan pendeteksian dan pengenalan kendaraan menggunakan pendekatan image processing, baik dengan data masukan berupa citra maupun video. Hasil yang diharapkan dapat menjadi acuan untuk peneliti yang hendak melakukan penelitian tentang pendeteksian dan pengenalan kendaraan.

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