Summary of Deegits: Deep Learning Based Framework For Measuring Heterogenous Traffic State in Challenging Traffic Scenarios, by Muttahirul Islam et al.
DEEGITS: Deep Learning based Framework for Measuring Heterogenous Traffic State in Challenging Traffic Scenarios
by Muttahirul Islam, Nazmul Haque, Md. Hadiuzzaman
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed DEEGITS framework combines state-of-the-art CNN techniques with data fusion, image preprocessing, and augmentation to accurately detect vehicles and pedestrians in challenging scenarios, such as congestion and occlusion. The model leverages transfer learning on a YOLOv8 pretrained model to identify diverse vehicles and employs the Grid Search algorithm to optimize hyperparameters. Experimental results demonstrate high accuracy within the detection framework, surpassing previous benchmarks on similar datasets. The DeepSORT multi-object tracking algorithm is incorporated to track detected vehicles and pedestrians, allowing for measurement of heterogeneous traffic states in mixed traffic conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DEEGITS is a new way to detect vehicles and pedestrians using deep learning. It’s like having superpower eyes that can see through obstacles! The team used special tricks to make the model better, like combining data from different sources and making sure it’s trained on lots of examples. They tested it in real-world scenarios and found that it was really accurate. Now they’re using this tool to measure traffic flow and speed in different areas. |
Keywords
» Artificial intelligence » Cnn » Deep learning » Grid search » Object tracking » Transfer learning
