  Infrared small target detection is a key technique in infrared search and
tracking (IRST) systems. Although deep learning has been widely used in the
vision tasks of visible light images recently, it is rarely used in infrared
small target detection due to the difficulty in learning small target features.
In this paper, we propose a novel lightweight convolutional neural network
TBC-Net for infrared small target detection. The TBCNet consists of a target
extraction module (TEM) and a semantic constraint module (SCM), which are used
to extract small targets from infrared images and to classify the extracted
target images during the training, respectively. Meanwhile, we propose a joint
loss function and a training method. The SCM imposes a semantic constraint on
TEM by combining the high-level classification task and solve the problem of
the difficulty to learn features caused by class imbalance problem. During the
training, the targets are extracted from the input image and then be classified
by SCM. During the inference, only the TEM is used to detect the small targets.
We also propose a data synthesis method to generate training data. The
experimental results show that compared with the traditional methods, TBC-Net
can better reduce the false alarm caused by complicated background, the
proposed network structure and joint loss have a significant improvement on
small target feature learning. Besides, TBC-Net can achieve real-time detection
on the NVIDIA Jetson AGX Xavier development board, which is suitable for
applications such as field research with drones equipped with infrared sensors.
