LPCV

Disasters like floods and earthquakes threaten human safety, infrastructure, and natural systems. Every year, disasters kill an average of 60,000 people, affect 200 million and cause $150 billion (USD) billion in damage. A timely and accurate understanding of the environment plays a key role in disaster preparedness, assessment and response. Recently, unmanned aerial vehicles (UAV) with inexpensive sensors have emerged as a practical tool to collect situational imagery from disaster areas that can be hard-to-reach for humans. However, UAVs are equipped with energy-constrained supplies and low-compute devices, which limit the ability to perform automatic on-device analysis. This adds to on-board system latency, resulting in longer response times for disaster relief efforts.. Therefore, achieving effective on-device computer vision with low power consumption and low latency remains a significant challenge.

To promote the community’s interest and progress toward an efficient and effective understanding of disaster scenes on UAV-based edge devices, we propose the On-device Disaster Scene Parsing Competition. Participants will devise models to improve semantic segmentation on an edge device (NVIDIA Jetson Nano 2GB Developer Kit) with a new disaster-scene dataset containing 1,700 samples collected by UAVs. The submitted models will be automatically benchmarked by evaluating their accuracy, speed, and power consumption. We hope our competition clarifies the challenges of UAV-view scene understanding with edge computing and spurs innovations for practical applications.

Tentative Schedule:

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Organizers