  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.
