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Journal articles on the topic 'Motorcycle Vehicle detection'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Shabestari, Zahra Badamchi, Ali Hosseininaveh, and Fabio Remondino. "Motorcycle Detection and Collision Warning Using Monocular Images from a Vehicle." Remote Sensing 15, no. 23 (2023): 5548. http://dx.doi.org/10.3390/rs15235548.

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Motorcycle detection and collision warning are essential features in advanced driver assistance systems (ADAS) to ensure road safety, especially in emergency situations. However, detecting motorcycles from videos captured from a car is challenging due to the varying shapes and appearances of motorcycles. In this paper, we propose an integrated and innovative remote sensing and artificial intelligence (AI) methodology for motorcycle detection and distance estimation based on visual data from a single camera installed in the back of a vehicle. Firstly, MD-TinyYOLOv4 is used for detecting motorcy

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Marayatr, Thepnimit, and Pinit Kumhom. "Motorcyclist's Helmet Wearing Detection Using Image Processing." Advanced Materials Research 931-932 (May 2014): 588–92. http://dx.doi.org/10.4028/www.scientific.net/amr.931-932.588.

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Motorcycle accidents have been rapidly growing throughout the years in many countries. Due to various social and economic factors, this type of vehicle is becoming increasingly popular. The helmet is the main safety equipment of motorcyclists but many drivers do not use it. If a motorcyclist is without helmet an accident can be fatal. This paper presented an automatic method for vehicle detection, motorcycles classification on public roads and a system for automatic detection of motorcyclists without helmet. For processing, in first step, we detect vehicles that moving real-time by extracting

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Masula, Jess, Reimar Tingga, Julie Ann Salido, Jing Chor Española, and Gazle Kent Gillesania. "Motorcycle Recognition System Using Convolutional Neural Network." Journal of Innovative Technology Convergence 6, no. 3 (2024): 91–98. http://dx.doi.org/10.69478/jitc2024v6n3a09.

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Vehicle detection is becoming increasingly important for highway management, particularly in Aklan, where motorcycles and tricycles are the predominant modes of transportation. Due to their diverse designs, accurate detection of these vehicles remains challenging. This study addresses this issue by developing a vision-based motorcycle recognition system using a Convolutional Neural Network (CNN) implemented in MATLAB. A new high-definition dataset, comprising 34,002 annotated instances from 17,785 motorcycle images and 16,217 tricycle images, was created. The dataset was collected from various

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Vali Y, Dr Sharmasth. "A Comprehensive IoT-Based Bike Crash Detection and Emergency Response System for Enhanced Road Safety." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem28064.

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Motorcycle travel, deemed one of the riskiest modes of transportation, faces a staggering fatality rate, with 212.7 deaths for every million miles travelled. Unlike enclosed vehicles, motorcycles expose riders to their surroundings, heightening the need for proactive safety measures. This paper explores the development and implementation of a Motorcycle Crash Detection and Alert System (MCDAS) utilizing the Multi-axes accelerometer. The system is designed to detect when a motorcycle falls and promptly alert emergency services and contacts via Firebase cloud. The study delves into the challenge

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Abdul Khalid, M. S., Z. H. Zulkipli, M. S. Solah, et al. "A Review of Motorcycle Safety Technologies from the Motorcycle and Passenger Car Perspectives." Journal of the Society of Automotive Engineers Malaysia 5, no. 3 (2021): 417–29. http://dx.doi.org/10.56381/jsaem.v5i3.184.

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   Motorcycle riders have been the top contributor to road deaths for over a decade in Malaysia. With proper safety technology systems installed on the upcoming models, it is predicted that motorcycle crashes and deaths can be reduced in the next decade. This study aims to review the past, recent, and upcoming vehicle safety technologies from the motorcycle and passenger car perspective that can potentially help reduce motorcycle crashes and injury risks. Various safety technologies have been introduced for passenger cars such as anti-lock braking systems, electronic stabilit

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Ballesteros, John R., German Sanchez-Torres, and John W. Branch-Bedoya. "HAGDAVS: Height-Augmented Geo-Located Dataset for Detection and Semantic Segmentation of Vehicles in Drone Aerial Orthomosaics." Data 7, no. 4 (2022): 50. http://dx.doi.org/10.3390/data7040050.

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Detection and Semantic Segmentation of vehicles in drone aerial orthomosaics has applications in a variety of fields such as security, traffic and parking management, urban planning, logistics, and transportation, among many others. This paper presents the HAGDAVS dataset fusing RGB spectral channel and Digital Surface Model DSM for the detection and segmentation of vehicles from aerial drone images, including three vehicle classes: cars, motorcycles, and ghosts (motorcycle or car). We supply DSM as an additional variable to be included in deep learning and computer vision models to increase i

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Nikita, Gosavi, Hirani Dhawal, Budhwant Ankita, Patil Rahul, and Joshi Harshal. "Helfine: Real-Time Helmet Violation Detection with Automated Fine System." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 2 (2025): 1413–20. https://doi.org/10.5281/zenodo.14964338.

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Motorcycles have traditionally been one of the most widely used modes of transportation in developing countries. However, in recent times, the number of motorcycle accidents has increased. One of the major contributing factors to these accidents is the absence of helmets worn by riders. Traffic authorities monitor road intersections, review CCTV footage, and take action against motorcyclists who fail to comply with helmet regulations. Enforcing this rule typically requires human intervention, making it a labor-intensive process. To address this issue, this project proposes an automated system

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Phalke, Ashwini, Vaishnavi Hambir, Namrata Utekar, Sudharshni Nadar, and Jueab Shaikh. "SAFEPARK: Vehicle Detection and Traffic Violation Parking Management System." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 4260–67. http://dx.doi.org/10.22214/ijraset.2024.61035.

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Abstract: The escalating motorcycle accident rates highlight the pressing need for improved safety measures. Helmets, a crucial safety gear, are often neglected, contributing significantly to fatalities. This paper addresses the pervasive issue of noncompliance with motorcycle safety rules, focusing on helmet usage and triple riding. Existing systems for monitoring lack precision, prompting our proposed Bike Traffic Violation System. Leveraging Haar Cascade and YOLOv3 models, it identifies motorcycles, detects riders without helmets, instances of triple riding, and even empty parking spots wit

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Wu, Bing-Fei, Chih-Chung Kao, Ying-Feng Li, and Min-Yu Tsai. "A Real-Time Embedded Blind Spot Safety Assistance System." International Journal of Vehicular Technology 2012 (April 22, 2012): 1–15. http://dx.doi.org/10.1155/2012/506235.

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This paper presents an effective vehicle and motorcycle detection system in the blind spot area in the daytime and nighttime scenes. The proposed method identifies vehicle and motorcycle by detecting the shadow and the edge features in the daytime, and the vehicle and motorcycle could be detected through locating the headlights at nighttime. First, shadow segmentation is performed to briefly locate the position of the vehicle. Then, the vertical and horizontal edges are utilized to verify the existence of the vehicle. After that, tracking procedure is operated to track the same vehicle in the

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Berge, Truls, and Viggo Henriksen. "NEMO project: acoustic detection of vehicle engine speed." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 5 (2021): 970–80. http://dx.doi.org/10.3397/in-2021-1718.

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As part of the EU Horizon2020 project NEMO, SINTEF has developed an algorithm to detect the engine speed of passing vehicles. Some road vehicles can emit abnormal high noise levels or high levels of exhaust gases in urban conditions. The high noise level can be related to aggressive driving (high acceleration and high engine speed), to a modified or malfunctioning exhaust system, or to other vehicle defects. It is well-known that many motorcycles or mopeds often are equipped with non-original exhaust mufflers, giving high noise levels that can be a nuisance to the community. In the NEMO projec

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Oktavia, Vessa Rizky, Ahmad Wali Satria Bahari Johan, Whisnumurty Galih Ananta, Fahril Refiandi, and Muhammad Khuluqil Karim. "Detection of Motorcycle Headlights Using YOLOv5 and HSV." Teknika 12, no. 3 (2023): 189–97. http://dx.doi.org/10.34148/teknika.v12i3.682.

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"Electronic Traffic Law Enforcement" (ETLE) denotes a mechanism that employs electronic technologies to implement traffic regulations. This commonly entails utilizing a range of electronic apparatuses like cameras, sensors, and automated setups to oversee and uphold traffic protocols, administer fines, and enhance road security. ETLE systems are frequently utilized for identifying and sanctioning infractions like exceeding speed limits, disregarding red lights, and turning off the headlights. In Indonesia, there is currently no dedicated system designed to detect traffic violation, especially

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Yeap, Lead Liang, Aida Mustapha, Salama A. Mostafa, and Mohammed Ahmed Jubair. "Performance Comparison between Deep Learning and Machine Learning Algorithms in Vehicle Detection." Journal of Advanced Research in Applied Sciences and Engineering Technology 62, no. 2 (2024): 123–35. https://doi.org/10.37934/araset.62.2.123135.

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The detection of vehicles is the most important aspect of a traffic monitoring system and the performance of the vehicle detection model is critical. This research aims to provide a clear view of the vehicle detection capability of deep learning approaches, YOLOv5 and YOLOv7, against machine learning approaches, Logistic Regression and Decision Tree. The detection models are trained with 7,319 vehicle image datasets with imbalanced classes: car, bus, motorcycle and truck. Both YOLOv5 and YOLOv7 vehicle detection algorithms are able to classify all vehicle classes well by a minimum of 76% true

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Charef, Ayoub, Zahi Jarir, and Mohamed Quafafou. "The Impact of Motorcycle Positioning on Start-Up Lost Time: The Empirical Case Study of Signalized Intersections in Marrakech using VISSIM." Engineering, Technology & Applied Science Research 14, no. 3 (2024): 14313–18. http://dx.doi.org/10.48084/etasr.7141.

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This study explores the influence of a high percentage of motorcycles on the traffic flow and congestion in Marrakech by examining the impact of motorcycle positioning in shaping urban traffic dynamics, in particular, the start-up lost time at signalized intersections. Different motorcycle positioning strategies are analyzed to improve intersection efficiency and safety. A twofold approach was followed to achieve this objective. First, empirical data were collected using computer vision techniques. Second, different strategies were simulated in VISSIM based on the collected data. The approach

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Gaonkar, Needhi U. "Road Traffic Analysis Using Computer Vision." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 2002–6. http://dx.doi.org/10.22214/ijraset.2021.37630.

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Abstract: Traffic analysis plays an important role in a transportation system for traffic management. Traffic analysis system using computer vision project paper proposes the video based data for vehicle detection and counting systems based on the computer vision. In most Transportation Systems cameras are installed in fixed locations. Vehicle detection is the most important requirement in traffic analysis part. Vehicle detection, tracking, classification and counting is very useful for people and government for traffic flow, highway monitoring, traffic planning. Vehicle analysis will supply w

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Joshue, Garcia-Pajuelo, and Paiva-Peredo Ernesto. "Comparison and evaluation of YOLO models for vehicle detection on bicycle paths." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3634–43. https://doi.org/10.11591/ijai.v13.i3.pp3634-3643.

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Non-permitted vehicles have taken over bicycle lanes in various Latin American cities as an alternative escape from traffic. Still, they do not foresee the risk to which they expose users of smaller vehicles, such as cyclists. Technological advancement has made researchers use deep learning (DL) to solve various problems in a city's traffic. However, no research has been found focusing on any issue of vehicles allowed or prohibited to travel on a bicycle lane. Therefore, in this article, the you only look once (YOLO) algorithm was used, taking the lightest models from the YOLOv4 to the most re

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Garcia-Pajuelo, Joshue, and Ernesto Alonso Paiva-Peredo. "Comparison and evaluation of YOLO models for vehicle detection on bicycle paths." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3634. http://dx.doi.org/10.11591/ijai.v13.i3.pp3634-3643.

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<p>Non-permitted vehicles have taken over bicycle lanes in various Latin American cities as an alternative escape from traffic. Still, they do not foresee the risk to which they expose users of smaller vehicles, such as cyclists. Technological advancement has made researchers use deep learning (DL) to solve various problems in a city's traffic. However, no research has been found focusing on any issue of vehicles allowed or prohibited to travel on a bicycle lane. Therefore, in this article, the you only look once (YOLO) algorithm was used, taking the lightest models from the YOLOv4 to th

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Wang, Hao, Zhengyu Li, and Jianwei Li. "Road car image target detection and recognition based on YOLOv8 deep learning algorithm." Applied and Computational Engineering 69, no. 1 (2024): 103–8. http://dx.doi.org/10.54254/2755-2721/69/20241489.

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In this paper, target detection of car images in roads is performed based on the YOLOv8 model of YOLO family of models, which improves the accuracy and generalisation of the target detection task by combining multi-scale prediction, CSPNet structure and optimisation techniques such as BoF and BoS. The input images contain five types of vehicles such as Ambulance, Bus, Car, Motorcycle and Truck, which are analysed and learnt to have a classification accuracy of 75.4% on Ambulance, 53.5% on Bus, 55.1% on Car, 51.1% on Motorcycle and 42.5% on Truck. Despite the gap in specific classification accu

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Khoiriyah, Rofiatul, and Aria Hendrawan. "Vehicle Detection on The Traffic Using Detection Transformer (DETR) Algorithm." International Journal of Artificial Intelligence and Science 1, no. 1 (2024): 14–24. https://doi.org/10.63158/ijais.v1.i1.4.

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Object detection is a computer vision technique aimed at detecting and identifying objects in images or videos. In recent years, with advancements in Machine Learning and Deep Learning, object detection has made significant progress in various fields such as healthcare, security, and transportation. The DETR algorithm is a novel approach in object detection that combines transformer architecture with attention techniques to address object detection challenges. This research applies the DETR algorithm with ResNet backbone for vehicle detection on the roads, involving 6 object classes: Car, Truc

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Suhadi, Suhadi, Muhamad Nur, Sulistyowati Sulistyowati, and Amat Suroso. "Matic motorcycle transmission damage detection system using internet of things-based expert system." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (2022): 1018. http://dx.doi.org/10.11591/ijeecs.v26.i2.pp1018-1026.

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The development of the motorcycle industry in Indonesia has developed quite significantly from year to year, the BPS (Central Statistics Agency) noted that in 2019 the number of motorcycle sales reached 106.657.952 units, and the AISI (Indonesian Motorcycle Industry Association) in 2020 the type of scooter (matic) of 2,696,557 units or 87.9%, this automatic type of motorbike is the largest contributor to two-wheeled vehicles and is the favorite type of vehicle for Indonesians. Automatic transmission or what is known as CVT (Continuous Variable Transmission), which is an automatic speed transfe

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Suhadi1, 3., Nur1 Muhamad, Sulistyowati1, and Suroso2 Amat. "Matic motorcycle transmission damage detection system using internet of things-based expert system." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (2022): 1018–26. https://doi.org/10.11591/ijeecs.v26.i2.pp1018-1026.

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The development of the motorcycle industry in Indonesia has developed quite significantly from year to year, the Central Statistics Agency noted that in 2019 the number of motorcycle sales reached 106,657,952 units, and the Indonesian motorcycle industry association (AISI) in 2020 the type of scooter (matic) of 2,696,557 units or 87.9%, this automatic type of motorbike is the largest contributor to two-wheeled vehicles and is the favorite type of vehicle for Indonesians. Automatic transmission or what is known as continuous variable transmission (CVT), is an automatic speed transfer system acc

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Ch. Sowmya, Marrivada Gayathri, Ejnavarjala Srilekha, and Shaik Obaid. "Real-Time Vehicle Detection and Classification in Traffic Videos Using Yolov8." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 06 (2025): 2282–86. https://doi.org/10.47392/irjaem.2025.0359.

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The Vehicle detection is important for the enhancement of transportation systems and which is efficient for traffic management, improved road safety and accurate data collection by automatically identifying and tracking vehicles on roads which enables features like traffic signal optimization, speed measurement and accident detection ultimately contributing to a smoother and safer driving experience for everyone. Here we have built a real-time project which can detect car, bus, motorcycle and truck on the basis of algorithm called YOLOV8 (You Only Look Once Version 8). It is a computer vision

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Guo, Haocheng, Yaqiong Zhang, Lieyang Chen, and Arfat Ahmad Khan. "Research on Vehicle Detection Based on Improved YOLOv8 Network." Applied and Computational Engineering 116, no. 1 (2025): 161–67. https://doi.org/10.54254/2755-2721/2025.20568.

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The key to ensuring the safe obstacle avoidance function of autonomous driving systems lies in the use of extremely accurate vehicle recognition techniques. However, the variability of the actual road environment and the diverse characteristics of vehicles and pedestrians together constitute a huge obstacle to improving detection accuracy, posing a serious challenge to the realization of this goal. To address the above issues, this paper proposes an improved YOLOv8 vehicle detection method. Specifically, taking the YOLOv8n-seg model as the base model, firstly, the FasterNet network is used to

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Fernando Maesa and Hartono Hartono. "Rancang Bangun Sistem Kontrol dan Monitoring Keamanan Sepeda Motor Berbasis IoT dengan Modul GPS Neo-6M dan Sensor Getar SW-420." JURNAL SURYA TEKNIKA 12, no. 1 (2025): 37–43. https://doi.org/10.37859/jst.v12i1.8532.

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Motorcycle security in Indonesia has become an important issue due to the high number of thefts, with 37,684 cases in November 2023, a sharp increase of 165% compared to the previous year. To address this issue, an Internet of Things (IoT)-based security system was developed that integrates the Neo-6M GPS module, SW-420 vibration sensor, and ESP32 WROOM microcontroller. The system enables real-time monitoring of vehicle position, suspicious activity detection, and instant notification delivery through a mobile application. The GPS module results have positioning accuracy with a margin of error

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Iirsyam, Muhammad. "PERANCANGAN ALAT PENDETEKSI KELAYAKAN OLI PADA KENDARAAN SEPEDA MOTOR BERBASIS ARDUINO UNO ATMEGA328." SIGMA TEKNIKA 2, no. 2 (2019): 179. http://dx.doi.org/10.33373/sigma.v2i2.2061.

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AbstrakPada kendaraan bermotor pelumasan adalah suatu hal yang sangat penting. Pelumasan berfungsi untuk melumasi komponen logam atau metal yang bergesekan dalam mesin. Untuk menjaga performa mesin kendaraan tetap prima maka kita harus cermat dalam memilih jenis oli yang akan digunakan menurut tingkat kekentalan oli yang dibutuhkan oleh kendaraan yang kita gunakan. Selain itu pemeriksaan dan pergantian oli secara berkala berperan penting dalam menjaga awetnya mesin kendaraan.Untuk membantu dalam pengecekan oli bagi para pemilik kendaran bermotor dalam hal ini sepeda motor dalam itu peneliti me

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Afiqah Omar and Fauziana Lamin. "Examining Fatal Motorcycle Crashes in Malaysia: Rider Age, Road Attributes and Collision Partner Dynamics." International Journal of Latest Technology in Engineering Management & Applied Science 14, no. 1 (2025): 1–6. https://doi.org/10.51583/ijltemas.2025.140101.

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Abstract: - High severity crashes involving motorcycles in Malaysia were analyzed to understand the crash characteristics, crash partner, and crash scenarios. This would involve studying how these variables influence the occurrence and severity of fatal crashes. Based on the examination, countermeasures are emphasized from the perspective of technologies, particularly those available on passenger vehicles. The objective of this study is to identify key risk factors and provide insights for improving road safety policies and intervention strategies. The highest age group involved in motorcycle

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Assubhi, Moh Hanif, and R. Rahmadewi. "PERANCANGAN SISTEM KENDALI PADA SISTEM KEAMANAN SEPEDA MOTOR DENGAN MIKROKONTROLER ESP32." Aisyah Journal Of Informatics and Electrical Engineering (A.J.I.E.E) 6, no. 1 (2024): 67–80. http://dx.doi.org/10.30604/jti.v6i1.168.

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Along with the rapid development of technology, of course, making the security system more sophisticated, efficient and innovative. Internet of Things (IoT)-based security is one of the options for implementing technology in the security sector. With the existence of IoT, every community can keep their vehicle safely through a protection or monitoring system. In this study, the authors design and manufacture a Motorcycle Security System using Face recognition and Bluetooth-based Keyless as a motorcycle lock and a SW-420 vibration sensor as a theft detection. The theft detection system is also

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Bokade, Rushikesh. "Street Pothole and Speed breaker detection with Theft Prevention Techniques: A Survey." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (2021): 1008–14. http://dx.doi.org/10.22214/ijraset.2021.38531.

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Abstract: Potholes are very harmful road surface conditions that prevent a safe, secure and reliable transportation and movement of people, goods and services. Road surface obstacles such as potholes affect the safety and comfort of most road users and commuters. Bad road networks hamper the smooth movement of goods and services and contribute to the poor growth and development of the economy whiles good road networks provides access to markets and enable fast and smooth transportation of goods and services from producers to consumers. Early detection and maintenance of potholes helps to creat

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Mansour Mohamed, Abuelgasim Saadeldin Mansour Mohamed, and Muhammad Mahbubur Rashid. "Video-Based Vehicle Counting and Analysis using YOLOv5 and DeepSORT with Deployment on Jetson Nano." Asian Journal of Electrical and Electronic Engineering 2, no. 2 (2022): 11–20. http://dx.doi.org/10.69955/ajoeee.2022.v2i2.34.

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In recent years, the advancements in deep learning and high-performance edge-computing systems have increased tremendously and have become the center of attention when it comes to the analysis of video-based systems on edge by making use of computer vision techniques. Intelligent Transportation Systems (ITS) is one area where deep learning can be used for several tasks including highway-based vehicle counting systems whereby making use of computer vision techniques, an edge computing device and cameras installed in specific locations on the road, we are able to obtain very accurate vehicle cou

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Rismayani, Rismayani, Muhammad Wahinuddin Tahir, Muhammad Darwis, Nurani Nurani, and Martina Pineng. "Model-View-Controller Design System of Motorcycle Damage Detection Using Forward Chaining Method." Journal of Information Technology and Its Utilization 6, no. 2 (2023): 51–59. http://dx.doi.org/10.56873/jitu.6.2.5230.

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This study aims to design a motorbike damage detection system using the forward chaining method with a view controller model that can be run on a mobile-based system. Dealers and motorbike service providers receive and fulfil customer requests for motorbike service services. Mechanics who service vehicles still use conventional methods to check vehicle damage by scanning the paper (form). There is a list ofvehicle damage. This method takes quite a long time, and it is not sure that the problem will be resolved quickly. The research method used is forward chaining, and the model used is the Mod

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Fachrie, Muhammad. "A Simple Vehicle Counting System Using Deep Learning with YOLOv3 Model." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 3 (2020): 462–68. http://dx.doi.org/10.29207/resti.v4i3.1871.

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Deep Learning is a popular Machine Learning algorithm that is widely used in many areas in current daily life. Its robust performance and ready-to-use frameworks and architectures enables many people to develop various Deep Learning-based software or systems to support human tasks and activities. Traffic monitoring is one area that utilizes Deep Learning for several purposes. By using cameras installed in some spots on the roads, many tasks such as vehicle counting, vehicle identification, traffic violation monitoring, vehicle speed monitoring, etc. can be realized. In this paper, we discuss a

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Wang, Bo, Yuan-Yuan Li, Weijie Xu, Huawei Wang, and Li Hu. "Vehicle–Pedestrian Detection Method Based on Improved YOLOv8." Electronics 13, no. 11 (2024): 2149. http://dx.doi.org/10.3390/electronics13112149.

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The YOLO series of target detection networks are widely used in transportation targets due to the advantages of high detection accuracy and good real-time performance. However, it also has some limitations, such as poor detection in scenes with large-scale variations, a large number of computational resources being consumed, and occupation of more storage space. To address these issues, this study uses the YOLOv8n model as the benchmark and makes the following four improvements: (1) embedding the BiFormer attention mechanism in the Neck layer to capture the associations and dependencies betwee

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Han, Yunfei, Tonghai Jiang, Yupeng Ma, and Chunxiang Xu. "Pretraining Convolutional Neural Networks for Image-Based Vehicle Classification." Advances in Multimedia 2018 (October 2, 2018): 1–10. http://dx.doi.org/10.1155/2018/3138278.

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Vehicle detection and classification are very important for analysis of vehicle behavior in intelligent transportation system, urban computing, etc. In this paper, an approach based on convolutional neural networks (CNNs) has been applied for vehicle classification. In order to achieve a more accurate classification, we removed the unrelated background as much as possible based on a trained object detection model. In addition, an unsupervised pretraining approach has been introduced to better initialize CNNs parameters to enhance the classification performance. Through the data enhancement on

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Hakim, Luqman, Aria Hendrawan, and Rofiatul Khoiriyah. "Traffic Vehicle Detection Using Faster R-CNN: A Comparative Analysis of Backbone Architectures." International Journal of Artificial Intelligence and Science 1, no. 1 (2024): 50–62. https://doi.org/10.63158/ijais.v1.i1.5.

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Object detection is a crucial task in computer vision, where advanced deep learning models have shown significant improvements over traditional methods. In this study, the Faster R-CNN algorithm is applied to a traffic dataset containing six vehicle categories: Bus, Car, Motorcycle, Pick Up Car, Truck, and Truck Box. The novelty of the research lies in the comparison of four backbone architectures ResNet50, ResNet50V2, MobileNetV3 Large, and MobileNetV3 Large 320 evaluated for their performance in vehicle detection at IoU thresholds of 0.5 and 0.75. The results reveal that ResNet50 provided th

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Maharani Raharja, Nia, Muhammad Arief Fathansyah, and Anna Nur Nazilah Chamim. "Vehicle Parking Security System with Face Recognition Detection Based on Eigenface Algorithm." Journal of Robotics and Control (JRC) 3, no. 1 (2021): 78–85. http://dx.doi.org/10.18196/jrc.v3i1.12681.

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RFID (Radio Frequency Identification) card technology is used for intelligent parking systems. Officers no longer need to record and check manually and repeatedly every vehicle that comes in and out with this system. The system has a weakness, namely if the RFId card is dropped or lost, the person who finds it can use the card. For the purpose of increasing security for users, one of the latest technologies is the use of facial recognition methods. The eigenface algorithm system is an algorithm used for face recognition. The algorithm is used as a training process for the previously inputted e

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Cárdenas-Lancheros, Esteban Alejandro, and Nelson Enrique Vera-Parra. "Incident forecasting model for motorcycle driving based on IoT and artificial intelligence." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 444. http://dx.doi.org/10.11591/ijeecs.v24.i1.pp444-451.

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Internet of things (IoT) and artificial intelligence provide more and more solutions to the exercise of capturing data effectively, taking them through processing and analysis stages to extract valuable information. Currently, technological tools are applied to counteract incidents in motorcycle driving, whether they are part of the same vehicle or are externally involved in the environment. Incidents in motorcycle driving are increasing due to the demand for the acquisition of these vehicles, which makes it important to generate an approach towards reducing the risk of road accidents based on

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Cárdenas-Lancheros, Esteban Alejandro, and Nelson Enrique Vera-Parra. "Incident forecasting model for motorcycle driving based on IoT and artificial intelligence." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 444–51. https://doi.org/10.11591/ijeecs.v24.i1.pp444-451.

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Internet of things (IoT) and artificial intelligence provide more and more solutions to the exercise of capturing data effectively, taking them through processing and analysis stages to extract valuable information. Currently, technological tools are applied to counteract incidents in motorcycle driving, whether they are part of the same vehicle or are externally involved in the environment. Incidents in motorcycle driving are increasing due to the demand for the acquisition of these vehicles, which makes it important to generate an approach towards reducing the risk of road accidents based on

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Karthika L. "Helmet detection using Image Processing and Deep Learning in Workplace." Journal of Information Systems Engineering and Management 10, no. 53s (2025): 541–51. https://doi.org/10.52783/jisem.v10i53s.10946.

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Ensuring compliance with mandatory helmet laws is critical for workplace and road safety. Traditional enforcement methods, which rely on manual inspections and surveillance personnel, are inefficient, error-prone, and challenging to scale with increasing vehicle and rider volumes. Additionally, factors such as poor image quality, varying viewing angles, and inconsistent monitoring further hinder effective enforcement. To address these challenges, this study proposes an automated helmet detection and motorcycle license plate recognition system leveraging deep learning techniques. A Convolutiona

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Angraini, Tuti, Zas Ressy Aidha, Anton, Dedi Kurniadi, and Cipto Prabowo. "Identification of Bioethanol Quality for Motorcycle Fuel." International Journal of Advanced Science Computing and Engineering 5, no. 3 (2023): 278–86. http://dx.doi.org/10.62527/ijasce.5.3.173.

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The availability of crude oil as a raw material for vehicle fuel is dwindling and limited in nature. One of the renewable energies worth developing is bioethanol, which is one of the alternative fuels that can be used as a biofuel and can be processed from plants containing starch and glucose. In this research, the entire bioethanol identification system in a sugar cane drip distillation apparatus was examined. The distillation process using MQ3 and MQ135 sensors resulted in an alcohol percentage of 42% and 46%. The maximum temperature measured by a thermocouple during distillation was 88°C, w

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R, Parthiban, Dr J. Sreerambabu, and M. Mohammed Riyaz. "Speed, Direction, Color and Type Identification of NHAI Expansions Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (2022): 763–67. http://dx.doi.org/10.22214/ijraset.2022.46281.

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Abstract: Vehicle numeration is associate interaction to appraise the road traffic thickness to judge the traffic conditions for shrewd transportation frameworks. With the broad use of cameras in metropolitan vehicle frameworks, the reconnaissance mission video has become a focal info supply to boot, constant traffic the board framework has become illustrious as lately owing to the accessibility of handheld/versatile cameras and machine learning investigation. In this work, propose video-based vehicle as well as technique in associate superhighway traffic video caught utilizing hand-held camer

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Buce, Christian Aaron G. "Tcu smart parking: a taguig city university vehicle parking lot organizer through plate detection." South Asian Journal of Engineering and Technology 12, no. 1 (2022): 1–4. http://dx.doi.org/10.26524/sajet.2022.12.1.

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The purpose of this project is to develop a new way/method in conducting a parking in the Taguig City University. For a more secure, more efficient, and easier way for both the operator of the system and the client. It lessens the illegal acts of criminals such as stealing a motorcycle. It also focused on determining the evaluation of the students, faculty members, and non teaching personnel on the developed TCU Smart Parking: A Taguig City University Vehicle Parking Lot Organizer Through Plate Detection using the criteria based on ISO 9126 software quality standard. A total of 60 students, 10

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Christian Aaron G Buce, Edizon Gasta, Joe Marie Dimalaluan, and Rexbelle Ibut. "Tcu smart parking: a taguig city university vehicle parking lot organizer through plate detection." South Asian Journal of Engineering and Technology 12, no. 1 (2022): 1–4. http://dx.doi.org/10.26524/sajet.2022.12.01.

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   The purpose of this project is to develop a new way/method in conducting a parking in the Taguig City University. For a more secure, more efficient, and easier way for both the operator of the system and the client. It lessens the illegal acts of criminals such as stealing a motorcycle. It also focused on determining the evaluation of the students, faculty members, and non teaching personnel on the developed TCU Smart Parking: A Taguig City University Vehicle Parking Lot Organizer Through Plate Detection using the criteria based on ISO 9126 software quality standard. A tot

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Kachapornkul, Seubsuang, Ruchao Pupadubsin, Pakasit Somsiri, Prapon Jitkreeyarn, and Kanokvate Tungpimolrut. "Performance Improvement of a Switched Reluctance Motor and Drive System Designed for an Electric Motorcycle." Energies 15, no. 3 (2022): 694. http://dx.doi.org/10.3390/en15030694.

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In this paper, the implementation of a switched reluctance motor (SRM) and drive system for the propulsion system of a two-seat electric motorcycle is described. The overall design focuses on the required vehicle speed, acceleration, driving distance, and overall system cost, as well as reliability. The performance of the three-phase 6/4 pole (six-stator pole and four-rotor pole) and four-phase 8/6 pole (eight-stator pole and six-rotor pole) are investigated and compared by static performance analysis and dynamic performance analysis. Their performance is further investigated by finite element

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Stemy, Simon Divya Kumaran A.K. "DETECTION OF MOTORCYCLISTS WITHOUT HELMET AND FINEPAYMENT USING OPEN CV." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES [AIVESC-18] (April 26, 2018): 28–32. https://doi.org/10.5281/zenodo.1230362.

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The helmet is the main safety equipment of motorcyclists, but many drivers do not use it. The main aim of this project is to construct an automatic detection of the motorcyclist without helmet from video using OpenCV library tools. If they are not wearing the helmet, the license plate of the motorcycle is focused automatically. By using Computer Vision technique we can detect and recognize the license plate number. We make the training set of different characters of different sizes. Based on these training set, we extracted the character from images and fine is to be cut-off from the user. Thi

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Chen, Shuai, Jinhui Lan, Haoting Liu, Chengkai Chen, and Xiaohan Wang. "Helmet Wearing Detection of Motorcycle Drivers Using Deep Learning Network with Residual Transformer-Spatial Attention." Drones 6, no. 12 (2022): 415. http://dx.doi.org/10.3390/drones6120415.

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Aiming at the existing problem of unmanned aerial vehicle (UAV) aerial photography for riders’ helmet wearing detection, a novel aerial remote sensing detection paradigm is proposed by combining super-resolution reconstruction, residual transformer-spatial attention, and you only look once version 5 (YOLOv5) image classifier. Due to its small target size, significant size change, and strong motion blur in UAV aerial images, the helmet detection model for riders has weak generalization ability and low accuracy. First, a ladder-type multi-attention network (LMNet) for target detection is designe

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Indra, Dolly, Herman Herman, and Firman Shantya Budi. "Implementasi Sistem Penghitung Kendaraan Otomatis Berbasis Computer Vision." Komputika : Jurnal Sistem Komputer 12, no. 1 (2023): 53–62. http://dx.doi.org/10.34010/komputika.v12i1.9082.

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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 vehic

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Shreyasa, Joshi, Mady Pranamya, and Rangaswamy Shanta. "Smart Traffic Signalling Using Computer Vision for Avoidance of Blind Spots." Journal of Transportation Engineering and Traffic Management 4, no. 2 (2023): 1–10. https://doi.org/10.5281/zenodo.7949622.

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<em>A blind spot is the area or zone on the road outside the driver’s field of vision. In essence, it is a space that is hidden from view from your windscreen, as well as from your rear-view and side-view mirrors. It is concealed by some of the car's frame. Blind spots can be big enough to easily obstruct your view of another car, motorcycle, bicycle, or pedestrian. By the time a driver realizes that another vehicle is approaching from the other end it might be too late to control the vehicles. It is found that many critical accidents are caused by this situation. The proposed system

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Aloufi, Nasser, Abdulaziz Alnori, and Abdullah Basuhail. "Enhancing Autonomous Vehicle Perception in Adverse Weather: A Multi Objectives Model for Integrated Weather Classification and Object Detection." Electronics 13, no. 15 (2024): 3063. http://dx.doi.org/10.3390/electronics13153063.

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Robust object detection and weather classification are essential for the safe operation of autonomous vehicles (AVs) in adverse weather conditions. While existing research often treats these tasks separately, this paper proposes a novel multi objectives model that treats weather classification and object detection as a single problem using only the AV camera sensing system. Our model offers enhanced efficiency and potential performance gains by integrating image quality assessment, Super-Resolution Generative Adversarial Network (SRGAN), and a modified version of You Only Look Once (YOLO) vers

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Mahajan, Ritesh, Anish Patil, Saheb Singh Sandhu, Om Telang, and Niranjan Samudre. "Sighted-Helmet Detection and E-Challan Application." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 3313–17. http://dx.doi.org/10.22214/ijraset.2023.50858.

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Abstract: Helmet detection is a technology that uses computer vision algorithms to automatically detect and identify individuals who are not wearing helmets while riding a two-wheeled vehicle. This technology is often used in conjunction with e-challan systems, which are electronic systems for issuing traffic violations and fines. By combining helmet detection with e-challan, law enforcement officials can more effectively enforce helmet laws and reduce the number of injuries and deaths caused by head injuries in motorcycle accidents. The technology can be used in various forms like CCTV camera

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Fahmida, Sultana Mim S. M. Naimur Rhaman Sayam Md. Tanvir Amin. "Traffic Participants Detection and Classification Using YOLO Neural Network." LC International Journal of STEM (ISSN: 2708-7123) 3, no. 2 (2022): 9–18. https://doi.org/10.5281/zenodo.7771342.

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One of the most important requirements for the next generation of traffic monitoring systems, autonomous driving technology and Advanced Driving Assistance Systems (ADAS) is the detection and classification of traffic participants. Although in the areas of object detection and classification research, tremendous progress has been made, we focused on a specific task of detecting and classifying traffic participants from traffic scenarios. In our work, we have chosen a Deep Convolutional Neural Networks – YOLOv4 (You Only Look Once Version 4), a object detection algorithm to detect and cla

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Cahyono, Andhika Putra, and Utomo Budiyanto. "Penghitungan Objek Berdasarkan Berdasarkan Jenis Kendaraan Bermotor pada CCTV Lalu Lintas Berbasis Pengolahan Citra Digital Menggunakan Metode Background Subtraction dan Blob Detection." JTIM : Jurnal Teknologi Informasi dan Multimedia 2, no. 2 (2020): 92–99. http://dx.doi.org/10.35746/jtim.v2i2.98.

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In the road traffic space which is often encountered by passing traffic type of vehicle. To find out the traffic conditions that are needed to calculate vehicle traffic, such as using counting or recording CCTV video. This continues the long and long process that was completed on the error data and the slow pace of traffic engineering decisions. This method is difficult to do in full because of the limited number of counters. This can be done by involving digital processing and CCTV video to be able to classify and transfer vehicle type objects. There are several methods for sharing object ima

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