TBC-Net: A real-time detector for infrared small target detection using semantic constraint
Mingxin Zhao, Li Cheng, Xu Yang, Peng Feng, Liyuan Liu, and Nanjian Wu

TL;DR
This paper introduces TBC-Net, a lightweight deep learning model for real-time infrared small target detection, combining semantic constraints and data synthesis to improve accuracy and reduce false alarms in complex backgrounds.
Contribution
The paper presents a novel TBC-Net architecture with a target extraction module and semantic constraint module, along with a joint loss function and data synthesis method for enhanced small target detection.
Findings
TBC-Net outperforms traditional methods in reducing false alarms.
Achieves real-time detection on NVIDIA Jetson AGX Xavier.
Improves small target feature learning with joint loss and semantic constraints.
Abstract
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…
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Taxonomy
TopicsInfrared Target Detection Methodologies · Advanced Measurement and Detection Methods · Thermography and Photoacoustic Techniques
