Summary of Finetuning Yolov9 For Vehicle Detection: Deep Learning For Intelligent Transportation Systems in Dhaka, Bangladesh, by Shahriar Ahmad Fahim
Finetuning YOLOv9 for Vehicle Detection: Deep Learning for Intelligent Transportation Systems in Dhaka, Bangladesh
by Shahriar Ahmad Fahim
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning model is developed to detect vehicles in Dhaka, Bangladesh’s megacity, using deep learning and artificial intelligence. The goal is to create an Intelligent Transportation System (ITS) that can understand traffic congestion and mobility patterns. A fine-tuned object detector called YOLOv9 is trained on a Bangladesh-based dataset and achieves state-of-the-art performance with a mean Average Precision of 0.934 at an Intersection over Union threshold of 0.5. The model is proposed to be deployed on closed circuit television cameras on roads, processing vehicle detection output data in a graph structure. This can help policymakers implement the system, addressing transportation challenges and supporting the “Smart Bangladesh Vision 2041” development plan. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team developed an AI-powered vehicle detector to help with traffic issues in Dhaka, one of the fastest-growing cities in the world. They used special machine learning techniques to train a model that can recognize vehicles on roads. The model was tested and did very well, even beating previous results from similar studies. The idea is to use this technology to make transportation safer and more efficient, which would be a big help for people living in Dhaka. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Mean average precision
