Transformers in Small Object Detection: A Benchmark and Survey of
  State-of-the-Art
Aref Miri Rekavandi Shima Rashidi Farid Boussaid Stephen Hoefs Emre Akbas Mohammed bennamoun
Transformers have rapidly gained popularity in computer vision, especially in
the field of object recognition and detection. Upon examining the outcomes of
state-of-the-art object detection methods, we noticed that transformers
consistently outperformed well-established CNN-based detectors in almost every
video or image dataset. While transformer-based approaches remain at the
forefront of small object detection (SOD) techniques, this paper aims to
explore the performance benefits offered by such extensive networks and
identify potential reasons for their SOD superiority. Small objects have been
identified as one of the most challenging object types in detection frameworks
due to their low visibility. We aim to investigate potential strategies that
could enhance transformers' performance in SOD. This survey presents a taxonomy
of over 60 research studies on developed transformers for the task of SOD,
spanning the years 2020 to 2023. These studies encompass a variety of detection
applications, including small object detection in generic images, aerial
images, medical images, active millimeter images, underwater images, and
videos. We also compile and present a list of 12 large-scale datasets suitable
for SOD that were overlooked in previous studies and compare the performance of
the reviewed studies using popular metrics such as mean Average Precision
(mAP), Frames Per Second (FPS), number of parameters, and more. Researchers can
keep track of newer studies on our web page, which is available at
\url{https://github.com/arekavandi/Transformer-SOD}.