SpirDet: Towards Efficient, Accurate and Lightweight Infrared Small
  Target Detector
Qianchen Mao Qiang Li Bingshu Wang Yongjun Zhang Tao Dai C. L. Philip Chen
In recent years, the detection of infrared small targets using deep learning
methods has garnered substantial attention due to notable advancements. To
improve the detection capability of small targets, these methods commonly
maintain a pathway that preserves high-resolution features of sparse and tiny
targets. However, it can result in redundant and expensive computations. To
tackle this challenge, we propose SpirDet, a novel approach for efficient
detection of infrared small targets. Specifically, to cope with the
computational redundancy issue, we employ a new dual-branch sparse decoder to
restore the feature map. Firstly, the fast branch directly predicts a sparse
map indicating potential small target locations (occupying only 0.5\% area of
the map). Secondly, the slow branch conducts fine-grained adjustments at the
positions indicated by the sparse map. Additionally, we design an lightweight
DO-RepEncoder based on reparameterization with the Downsampling Orthogonality,
which can effectively reduce memory consumption and inference latency.
Extensive experiments show that the proposed SpirDet significantly outperforms
state-of-the-art models while achieving faster inference speed and fewer
parameters. For example, on the IRSTD-1K dataset, SpirDet improves $MIoU$ by
4.7 and has a $7\times$ $FPS$ acceleration compared to the previous
state-of-the-art model. The code will be open to the public.