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.

中文翻译：

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TBC-Net：使用语义约束进行红外小目标检测的实时检测器

红外小目标检测是红外搜索和跟踪（IRST）系统中的一项关键技术。虽然最近深度学习在可见光图像的视觉任务中得到了广泛的应用，但由于难以学习小目标特征，因此很少用于红外小目标检测。在本文中，我们提出了一种用于红外小目标检测的新型轻量级卷积神经网络 TBC-Net。TBCNet由目标提取模块（TEM）和语义约束模块（SCM）组成，分别用于从红外图像中提取小目标并在训练过程中对提取的目标图像进行分类。同时，我们提出了联合损失函数和训练方法。SCM通过结合高级分类任务对TEM施加语义约束，解决类不平衡问题导致特征学习困难的问题。在训练过程中，从输入图像中提取目标，然后由 SCM 进行分类。在推理过程中，仅使用 TEM 来检测小目标。我们还提出了一种数据合成方法来生成训练数据。实验结果表明，与传统方法相比，TBC-Net可以更好地减少复杂背景带来的误报，所提出的网络结构和联合损失对小目标特征学习有显着的提升。另外TBC-Net可以在NVIDIA Jetson AGX Xavier开发板上实现实时检测，