  The usage of drones has tremendously increased in different sectors spanning
from military to industrial applications. Despite all the benefits they offer,
their misuse can lead to mishaps, and tackling them becomes more challenging
particularly at night due to their small size and low visibility conditions. To
overcome those limitations and improve the detection accuracy at night, we
propose an object detector called Ghost Auto Anchor Network (GAANet) for
infrared (IR) images. The detector uses a YOLOv5 core to address challenges in
object detection for IR images, such as poor accuracy and a high false alarm
rate caused by extended altitudes, poor lighting, and low image resolution. To
improve performance, we implemented auto anchor calculation, modified the
conventional convolution block to ghost-convolution, adjusted the input channel
size, and used the AdamW optimizer. To enhance the precision of multiscale tiny
object recognition, we also introduced an additional extra-small object feature
extractor and detector. Experimental results in a custom IR dataset with
multiple classes (birds, drones, planes, and helicopters) demonstrate that
GAANet shows improvement compared to state-of-the-art detectors. In comparison
to GhostNet-YOLOv5, GAANet has higher overall mean average precision (mAP@50),
recall, and precision around 2.5\%, 2.3\%, and 1.4\%, respectively. The dataset
and code for this paper are available as open source at
https://github.com/ZeeshanKaleem/GhostAutoAnchorNet.
