Real-Time Pothole Detection at night to prevent road accidents using YOLOV5
- Resource Type
- Conference
- Authors
- Mandal, Gouranga; De, Anurag; Dey, Arindam; Gangarapu, Sudeepthi; Chekuru, Sharmila Rani; Nandini, Chekuri
- Source
- 2023 6th International Conference on Recent Trends in Advance Computing (ICRTAC) Recent Trends in Advance Computing (ICRTAC), 2023 6th International Conference on. :247-253 Dec, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
General Topics for Engineers
Power, Energy and Industry Applications
YOLO
Road accidents
Market research
Real-time systems
Object recognition
YOLOV5
Common Object in Context (COCO)
Image acquisition
pothole detection
Pytorch Framework
- Language
This article proposes a technique to create a pothole detection system that can operate both during the day and at night. This model was developed using the real-time object identification YOLO (You Only Look Once) technique. The potholes are located using a pre-trained model with YOLO v5. In its creation, the PyTorch framework is utilized. The first YOLO model created using the PyTorch framework is YOLOv5, which is a lot more user-friendly and lightweight. In a manner similar to real-time object detection systems, the proposed method displays potholes in real time and emphasizes them with boundary boxes for accurate identification. The accuracy achieved with this method is 97% during the day and 92% at night which is found to be satisfactory.