mlsanityData to 14 Oct 2026

Characterization of Semantic Segmentation Models on Mobile Platforms for Self-Navigation in Disaster-Struck Zones

arXiv 2202.01421 ↗First posted 3 Feb 2022cs.CV cs.ARcited by 1

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Abstract (arXiv)

The role of unmanned vehicles for searching and localizing the victims in disaster impacted areas such as earthquake-struck zones is getting more important. Self-navigation on an earthquake zone has a unique challenge of detecting irregularly shaped obstacles such as road cracks, debris on the streets, and water puddles. In this paper, we characterize a number of state-of-the-art FCN models on mobile embedded platforms for self-navigation at these sites containing extremely irregular obstacles. We evaluate the models in terms of accuracy, performance, and energy efficiency. We present a few optimizations for our designed vision system. Lastly, we discuss the trade-offs of these models for a couple of mobile platforms that can each perform self-navigation. To enable vehicles to safely navigate earthquake-struck zones, we compiled a new annotated image database of various earthquake impacted regions that is different than traditional road damage databases. We train our database with a number of state-of-the-art semantic segmentation models in order to identify obstacles unique to earthquake-struck zones. Based on the statistics and tradeoffs, an optimal CNN model is selected for the mobile vehicular platforms, which we apply to both low-power and extremely low-power configurations of our design. To our best knowledge, this is the first study that identifies unique challenges and discusses the accuracy, performance, and energy impact of edge-based self-navigation mobile vehicles for earthquake-struck zones. Our proposed database and trained models are publicly available.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 30 references that are in the map. First posted on arXiv 3 Feb 2022.

Builds on

14 of 30 references.

References · 14

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2 more
  1. 2020Transfer Learning-based Road Damage Detection for Multiple Countriescited by 6
  2. 2018Efficient Semantic Segmentation for Visual Bird's-eye View Interpretationcited by 3

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  1. 2025WarNav: An Autonomous Driving Benchmark for Segmentation of Navigable Zones in War Scenescited by 0