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Proceedings Paper

Small target detection for search and rescue operations using distributed deep learning and synthetic data generation
Author(s): Kyongsik Yun; Luan Nguyen; Tuan Nguyen; Doyoung Kim; Sarah Eldin; Alexander Huyen; Thomas Lu; Edward Chow
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Paper Abstract

It is important to find the target as soon as possible for search and rescue operations. Surveillance camera systems and unmanned aerial vehicles (UAVs) are used to support search and rescue. Automatic object detection is important because a person cannot monitor multiple surveillance screens simultaneously for 24 hours. Also, the object is often too small to be recognized by the human eye on the surveillance screen. This study used an UAVs around the Port of Houston and fixed surveillance cameras to build an automatic target detection system that supports the US Coast Guard (USCG) to help find targets (e.g., person overboard). We combined image segmentation, enhancement, and convolution neural networks to reduce detection time to detect small targets. We compared the performance between the autodetection system and the human eye. Our system detected the target within 8 seconds, but the human eye detected the target within 25 seconds. Our systems also used synthetic data generation and data augmentation techniques to improve target detection accuracy. This solution may help the search and rescue operations of the first responders in a timely manner.

Paper Details

Date Published: 13 May 2019
PDF: 6 pages
Proc. SPIE 10995, Pattern Recognition and Tracking XXX, 1099507 (13 May 2019); doi: 10.1117/12.2520250
Show Author Affiliations
Kyongsik Yun, Jet Propulsion Lab. (United States)
Luan Nguyen, Jet Propulsion Lab. (United States)
Tuan Nguyen, Jet Propulsion Lab. (United States)
Doyoung Kim, Jet Propulsion Lab. (United States)
Sarah Eldin, Jet Propulsion Lab. (United States)
Alexander Huyen, Jet Propulsion Lab. (United States)
Thomas Lu, Jet Propulsion Lab. (United States)
Edward Chow, Jet Propulsion Lab. (United States)


Published in SPIE Proceedings Vol. 10995:
Pattern Recognition and Tracking XXX
Mohammad S. Alam, Editor(s)

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