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

Abstract

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 Fully Convolutional Network (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 compile 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 FCN model is selected and applied to the mobile vehicular platforms. 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.INDEX TERMS Convolutional Neural Networks, Edge Computing, Self Driving, Semantic Segmentation I. INTRODUCTIONWith global-wide monitoring and technology evolution, the riskiness of natural disaster is getting lower. However, there are still deadly incidents such as earthquakes. Earthquakes are a geologic inevitability of some countries and states. To reduce the effects from earthquakes, we should think about the ways to quickly search earthquake-struck zones and safely rescue more lives. For this purpose, unmanned autonomous vehicles will be effective, which navigate impacted sites while localizing the people and reporting the damages without risking the lives of others, such as firefighters and other rescue workers. In the natural disaster impacted sites, edge-based small automobiles would be more cost effective and feasible solutions because these small cars can navigate underneath collapsed buildings, unstable bridges, and narrow walkways that are difficult to be driven in by a full-sized vehicle, and also hidden from the viewing angles of aerial solutions such as drones. Though the self-navigation research and industry has been quickly evolving, most of the solutions focus on detecting fairly regular-shaped objects such as hu-mans, buildings, and streets from a full-sized vehicle's perspective. However, these solutions cannot be directly applied to earthquake-struck zones because of the unique obstacles such as debris, cracks, and puddles on the streets, which the mobile edge vehicle perceive...

Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning

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