Vehicle counting method based on attention mechanism SSD and state detection

Liang, Ruiqiang; Huang, Lin

doi:10.1007/s00371-021-02161-y

Cited by 6 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Peneliti yang melakukan pendeteksian kendaraan menggunakan Mask-Regional Convolutional Neural Network (MRCNN) misalnya sistem pendeteksi objek pada mobil otonom dengan menggunakan kamera termal inframerah [15], sistem deteksi kendaraan menggunakan proses segmentasi untuk sistem kendaraan cerdas [16]. Peneliti melakukan pendeteksian kendaraan mengunakan Single Shot Multibox Detector (SSD) MobileNet misalnya sistem penghitung kendaraan [17], sistem pendeteksian kendaraan [18].…”

Section: Pendahuluanunclassified

Implementasi Sistem Penghitung Kendaraan Otomatis Berbasis Computer Vision

Indra¹,

Herman²,

Budi³

2023

Komputika

View full text Add to dashboard Cite

The development of computer technology today is very helpful for humans in completing their work in various fields. One application of computer technology i.e., in the field of computer vision which has a very important role for object recognition. In this study, we designed a computer vision-based automatic vehicle counting system. The system that we created uses the MobileNetV2 Single Shot Multibox Detector (SSD) which is placed on the Raspberry Pi 4 to carry out the process of classifying cars and motorcycles and the raspberry pi 4 also functions as a system controller. This automatic vehicle counter system has been integrated between Raspberry Pi 4 and a mobile application on a smartphone where the smartphone functions to display information such as day, date, month, year and together with the number of cars and motorcycles. We tested this automatic vehicle counting system on steam services (car and motorcycle washing) for 3 days where 10 vehicles were collected every day. The test results show that the system is capable of detecting cars and motorcyles with an average accuracy rate of 46.6%.

show abstract