YOLOv5-Based Electric Scooter Crackdown Platform
As the use of personal mobility (PM) devices continues to rise, regulatory violations have become more frequent, highlighting the need for technological solutions to ensure efficient enforcement. This study addresses these challenges by proposing an AI-based enforcement platform. The system integrat...
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| Format: | Article |
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/6/3112 |
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| author | Seung-Hyun Lee Sung-Hyun Oh Jeong-Gon Kim |
| author_facet | Seung-Hyun Lee Sung-Hyun Oh Jeong-Gon Kim |
| author_sort | Seung-Hyun Lee |
| collection | DOAJ |
| description | As the use of personal mobility (PM) devices continues to rise, regulatory violations have become more frequent, highlighting the need for technological solutions to ensure efficient enforcement. This study addresses these challenges by proposing an AI-based enforcement platform. The system integrates the You Only Look Once version 5 (YOLOv5) object detection model, a deep-learning-based framework, with Global Positioning System (GPS) location data, Raspberry Pi 5, and Amazon Web Services (AWS) for data processing and web-based implementation. The YOLOv5 model was deployed in two configurations: one for detecting electric scooter usage and another for identifying legal violations. The system utilized AWS Relational Database Service (RDS), Simple Storage Service (S3), and Elastic Compute Cloud (EC2) to store violation records and host web applications. The detection performance was evaluated using mean average precision (mAP) metrics. The electric scooter detection model achieved mAP50 and mAP50-95 scores of 99.5 and 99.457, respectively. Meanwhile, the legal violation detection model attained mAP50 and mAP50-95 scores of 99.5 and 81.813, indicating relatively lower accuracy for fine-grained violation detection. This study presents a practical technological platform for monitoring regulatory compliance and automating fine enforcement for shared electric scooters. Future improvements in object detection accuracy and real-time processing capabilities are expected to enhance the system’s overall reliability. |
| format | Article |
| id | doaj-art-81b7ca2689f74f1cb6b976e6d768da3f |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-81b7ca2689f74f1cb6b976e6d768da3f2025-08-20T02:11:18ZengMDPI AGApplied Sciences2076-34172025-03-01156311210.3390/app15063112YOLOv5-Based Electric Scooter Crackdown PlatformSeung-Hyun Lee0Sung-Hyun Oh1Jeong-Gon Kim2Department of Electronic Engineering, Tech University of Korea, Siheung-si 15297, Republic of KoreaDepartment of Electronic Engineering, Tech University of Korea, Siheung-si 15297, Republic of KoreaDepartment of Electronic Engineering, Tech University of Korea, Siheung-si 15297, Republic of KoreaAs the use of personal mobility (PM) devices continues to rise, regulatory violations have become more frequent, highlighting the need for technological solutions to ensure efficient enforcement. This study addresses these challenges by proposing an AI-based enforcement platform. The system integrates the You Only Look Once version 5 (YOLOv5) object detection model, a deep-learning-based framework, with Global Positioning System (GPS) location data, Raspberry Pi 5, and Amazon Web Services (AWS) for data processing and web-based implementation. The YOLOv5 model was deployed in two configurations: one for detecting electric scooter usage and another for identifying legal violations. The system utilized AWS Relational Database Service (RDS), Simple Storage Service (S3), and Elastic Compute Cloud (EC2) to store violation records and host web applications. The detection performance was evaluated using mean average precision (mAP) metrics. The electric scooter detection model achieved mAP50 and mAP50-95 scores of 99.5 and 99.457, respectively. Meanwhile, the legal violation detection model attained mAP50 and mAP50-95 scores of 99.5 and 81.813, indicating relatively lower accuracy for fine-grained violation detection. This study presents a practical technological platform for monitoring regulatory compliance and automating fine enforcement for shared electric scooters. Future improvements in object detection accuracy and real-time processing capabilities are expected to enhance the system’s overall reliability.https://www.mdpi.com/2076-3417/15/6/3112artificial intelligence (AI)object detectionpersonal mobility (PM)You Only Look Once version 5 (YOLOv5)Amazon Web Services (AWS) |
| spellingShingle | Seung-Hyun Lee Sung-Hyun Oh Jeong-Gon Kim YOLOv5-Based Electric Scooter Crackdown Platform Applied Sciences artificial intelligence (AI) object detection personal mobility (PM) You Only Look Once version 5 (YOLOv5) Amazon Web Services (AWS) |
| title | YOLOv5-Based Electric Scooter Crackdown Platform |
| title_full | YOLOv5-Based Electric Scooter Crackdown Platform |
| title_fullStr | YOLOv5-Based Electric Scooter Crackdown Platform |
| title_full_unstemmed | YOLOv5-Based Electric Scooter Crackdown Platform |
| title_short | YOLOv5-Based Electric Scooter Crackdown Platform |
| title_sort | yolov5 based electric scooter crackdown platform |
| topic | artificial intelligence (AI) object detection personal mobility (PM) You Only Look Once version 5 (YOLOv5) Amazon Web Services (AWS) |
| url | https://www.mdpi.com/2076-3417/15/6/3112 |
| work_keys_str_mv | AT seunghyunlee yolov5basedelectricscootercrackdownplatform AT sunghyunoh yolov5basedelectricscootercrackdownplatform AT jeonggonkim yolov5basedelectricscootercrackdownplatform |