Towards Robust Infrared Small Target Detection: A Feature-Enhanced and Sensitivity-Tunable Framework
This work addresses the problem of detecting small targets in infrared images for applications like surveillance, but it is incremental as it builds on existing deep learning methods.
The paper tackles infrared small target detection by proposing a feature-enhanced and sensitivity-tunable framework that improves robustness through multi-scale fusion and adjustable post-processing, achieving enhanced detection rates while maintaining segmentation accuracy in experiments.
Recently, single-frame infrared small target (SIRST) detection technology has attracted widespread attention. Different from most existing deep learning-based methods that focus on improving network architectures, we propose a feature-enhanced and sensitivity-tunable (FEST) framework, which is compatible with existing SIRST detection networks and further enhances their detection performance. The FEST framework improves the model's robustness from two aspects: feature enhancement and target confidence regulation. For feature enhancement, we employ a multi-scale fusion strategy to improve the model's perception to multi-scale features of multi-size targets, and design an edge enhancement difficulty mining (EEDM) loss to guide the network to continuously focus on challenging target regions and edge features during training. For target confidence regulation, an adjustable sensitivity (AS) strategy is proposed for network post-processing. This strategy enhances the model's adaptability in complex scenarios and significantly improves the detection rate of infrared small targets while maintaining segmentation accuracy. Extensive experimental results show that our FEST framework can effectively enhance the performance of existing SIRST detection networks. The code is available at https://github.com/YuChuang1205/FEST-Framework