Object Detection in Thermal Images Using Deep Learning for Unmanned
  Aerial Vehicles
Minh Dang Tu Kieu Trang Le Manh Duong Phung
This work presents a neural network model capable of recognizing small and
tiny objects in thermal images collected by unmanned aerial vehicles. Our model
consists of three parts, the backbone, the neck, and the prediction head. The
backbone is developed based on the structure of YOLOv5 combined with the use of
a transformer encoder at the end. The neck includes a BI-FPN block combined
with the use of a sliding window and a transformer to increase the information
fed into the prediction head. The prediction head carries out the detection by
evaluating feature maps with the Sigmoid function. The use of transformers with
attention and sliding windows increases recognition accuracy while keeping the
model at a reasonable number of parameters and computation requirements for
embedded systems. Experiments conducted on public dataset VEDAI and our
collected datasets show that our model has a higher accuracy than
state-of-the-art methods such as ResNet, Faster RCNN, ComNet, ViT, YOLOv5,
SMPNet, and DPNetV3. Experiments on the embedded computer Jetson AGX show that
our model achieves a real-time computation speed with a stability rate of over
90%.