Summary of Advancing Object Detection in Transportation with Multimodal Large Language Models (mllms): a Comprehensive Review and Empirical Testing, by Huthaifa I. Ashqar et al.
Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing
by Huthaifa I. Ashqar, Ahmed Jaber, Taqwa I. Alhadidi, Mohammed Elhenawy
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 This study investigates the application of multimodal large language models (MLLMs) and Large Vision Models (VLMs) in object detection for transportation systems. The authors provide a comprehensive review of MLLM technologies, highlighting their effectiveness and limitations in various transportation scenarios. They propose empirical analysis on three real-world transportation problems, including road safety attributes extraction, safety-critical event detection, and visual reasoning of thermal images. The findings provide a detailed assessment of MLLM performance, revealing both strengths and areas for improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how to use special kinds of artificial intelligence (AI) models to help make transportation systems better. These AI models are really good at understanding and processing lots of different types of data, like pictures and words. The authors look at what these models can do and what they’re not so good at when it comes to things like finding objects on roads, detecting safety problems, and making sense of thermal images (like the ones taken by drones). They find that these AI models are pretty good at some things, but still have some issues. The authors think this is important research because it can help make transportation systems safer and more efficient. |
Keywords
» Artificial intelligence » Event detection » Object detection
