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ACCIDENT DETECTION AND NOTIFICATION SYSTEM USING DEEP LEARNING TECHNIQUE |
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| รหัสดีโอไอ | |
| Creator | Pornpanom Nanthasen |
| Title | ACCIDENT DETECTION AND NOTIFICATION SYSTEM USING DEEP LEARNING TECHNIQUE |
| Contributor | Panomkhawn Riyamongkol |
| Publisher | Pibulsongkram Rajabhat University |
| Publication Year | 2566 |
| Journal Title | Life Sciences and Environment Journal |
| Journal Vol. | 24 |
| Journal No. | 2 |
| Page no. | 338-351 |
| Keyword | Road accident detection system, Road accident notification system, Deep learning |
| URL Website | https://ph01.tci-thaijo.org/index.php/psru/index |
| Website title | Life Sciences and Environment Journal |
| ISSN | 2773-9201 |
| Abstract | This study aimed to develop an accident detection and notification system for road accidents related to vulnerable groups on the road, such as motorcyclists, who account for 80% of all road fatalities in Thailand. YOLOv5 is used to develop a system where objects in the image can be detected, whether a person or a motorcycle involved in an accident. The comparison of accident detection results obtained with different YOLOv5 models led to the selection of the most suitable model. Then, the notification system was developed in Python language along with LINE Notify API (Line Notify Application Programming Interface) for sending images and notifications to groups of people through the Line application when the system detects a road accident. The results show that YOLOv5x has the best performance in accident detection with 93.21% compared to the results of other models. Moreover, the developed system was 100 percent successful in sending images along with a short message indicating the number of motorcycles and the number of people involved in the accident. This intelligent accident detection and notification system can detect accidents immediately by alerting rescue workers, police officers or other parties. The information about road accidents, including images or messages, can help evaluate the situation and promptly prepare the rescue team and the necessary equipment, leading to immediate assistance to accident victims. |
