Automatic Vehicle Classification and Counting System Using Inception Model

Rifai, Moch. Imam; Sudibyo, Rahardhita Widyatra; Kurniawan, Arasy Dafa Sulistya; Hadi, Moch. Zen Samsono; Mahmudah, Haniah; Sa'Adah, Nihayatus

doi:10.1109/icitee56407.2022.9954089

Cited by 1 publication

(6 citation statements)

References 6 publications

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“…The experiment setup video was shot on Manyar Kertoarjo in Surabaya, East Java, Indonesia. The system processes the results of the recording of road traffic conditions to perform operations to classify vehicle types and calculate the number of vehicles [8].…”

Section: Mobilenet V2 Modelmentioning

confidence: 99%

“…The camera is angled towards the road at about 45 degrees. The video used in the testing process lasts 2 minutes and 16 seconds [8].…”

Section: Mobilenet V2 Modelmentioning

confidence: 99%

“…Although the video for the testing process is only about 2 minutes long, there are already vehicles in the video such as motorcycles, cars, buses, and trucks that can be used to see the results of the classification using the MobileNet V2 model. Figure 2 depicts the installation of a camera to record road traffic conditions [8].…”

Section: Mobilenet V2 Modelmentioning

confidence: 99%

“…This method has a significant advantage when used as a model in the development and implementation of embedded systems. Because it not only supports fast data processing but also increased detection accuracy and devices with low-end hardware [8]- [10].…”

Section: A Introductionmentioning

confidence: 99%

“…The goal of this research is to develop edge computing systems [8] by improving the system's performance in the calculation and classification of vehicles using the SSD MobileNet V2 model. The system is made up of several devices that work together to improve each other's performance.…”

Section: A Introductionmentioning

confidence: 99%

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Edge Computing-Based Automated Vehicle Classification System Using the MobileNet V2 Model

Sudibyo

Mahmudah

Hadi

et al. 2022

ijcs

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The volume of traffic in one day is referred to as the average daily traffic volume. The Average Daily Traffic System (LHR) is also used to detect road damage caused by excessive vehicle loads. In the LHR system, vehicle data is still collected manually, with humans calculating the type and number of vehicles based on observations made and then divided into a time span. As a result, a system with a camera and deep learning data processing is required to automatically calculate the type and number of vehicles. The goal of this research is to develop edge computing systems by improving the system's performance in the calculation and classification of vehicles using the SSD MobileNet V2 model. The results of the MobileNet model scenario 5 have the lowest loss value of the five scenarios. The MobileNet V2 model can better classify vehicle types with a 65 FPS inference process.

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Section: Mobilenet V2 Modelmentioning

confidence: 99%

“…The camera is angled towards the road at about 45 degrees. The video used in the testing process lasts 2 minutes and 16 seconds [8].…”

Section: Mobilenet V2 Modelmentioning

confidence: 99%

Section: Mobilenet V2 Modelmentioning

confidence: 99%

Section: A Introductionmentioning

confidence: 99%

Section: A Introductionmentioning

confidence: 99%