EHDNet: Enhanced Human Detection Network for Search and Rescue
Seungoh Han, Ah-Young Nho, Wei Teng Kwan, Benjamin Paglia, Jacob Visniski, Minji Lee, Eric T. Matson, Minsun Lee · 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) · 2022
Human resources are a necessary cost for the search and rescue of people lost or stranded in the forest, which cannot be easily accessed in person. UAVs can be used to observe forests from up in the air without visiting directly. While UAVs are fly searching for people, they cannot be easily detected due to being too small and potentially being overlapped by obstacles such as grass, cars, and even the shadows of trees. We focus on detecting small objects even overlapped or hindered in the field and design an enhanced human detection Network (EHDNet) focusing on small objects. EHDNet enhances detection performance via an attentive feature pyramid network (AFPN) focused on small objects.