Small Target Detection for Search and Rescue Operations using Distributed Deep Learning and Synthetic Data Generation
This addresses the need for faster and more reliable target detection for first responders like the US Coast Guard, though it appears incremental as it combines existing techniques.
The study tackled the problem of detecting small targets like persons overboard in search and rescue operations using UAVs and surveillance cameras, achieving a detection time of 8 seconds compared to 25 seconds for the human eye.
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 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 auto-detection 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.