Summary of Icpr 2024 Competition on Safe Segmentation Of Drive Scenes in Unstructured Traffic and Adverse Weather Conditions, by Furqan Ahmed Shaik et al.
ICPR 2024 Competition on Safe Segmentation of Drive Scenes in Unstructured Traffic and Adverse Weather Conditions
by Furqan Ahmed Shaik, Sandeep Nagar, Aiswarya Maturi, Harshit Kumar Sankhla, Dibyendu Ghosh, Anshuman Majumdar, Srikanth Vidapanakal, Kunal Chaudhary, Sunny Manchanda, Girish Varma
First submitted to arxiv on: 9 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 The ICPR 2024 Competition on Safe Segmentation of Drive Scenes is a benchmarking platform for evaluating state-of-the-art semantic segmentation models under challenging conditions for autonomous driving. The competition uses the IDD-AW dataset, consisting of 5000 high-quality RGB-NIR image pairs captured under adverse weather conditions like rain, fog, low light, and snow. Participants were tasked with improving the Safe mean Intersection over Union (Safe mIoU) metric, which penalizes unsafe incorrect predictions that could be overlooked by traditional mIoU. The competition showcases advancements in semantic segmentation and prioritizes safety and robustness in unstructured and adverse conditions. The results set new benchmarks in the domain, highlighting the critical role of safety in deploying autonomous vehicles in real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The ICPR 2024 Competition on Safe Segmentation of Drive Scenes is a challenge to test how well computers can understand images taken under bad weather or weird lighting conditions for self-driving cars. The competition uses a special dataset with 5000 pictures and wants participants to improve the way they measure how good their computer models are at understanding these images. The twist is that this new way of measuring, called Safe mIoU, is designed to catch mistakes that could be dangerous if an autonomous car made them in real life. This competition shows how well computers can understand images under different conditions and how important it is for self-driving cars to be safe. |
Keywords
» Artificial intelligence » Semantic segmentation
