Real-Time Long-Wave Infrared Semantic Segmentation With Adaptive Noise Reduction and Feature Fusion

Semantic segmentation in the Long-Wave Infrared (LWIR) domain is critical for a wide range of applications including emergency response, industrial safety monitoring, and public building security. However, LWIR images often suffer from inherent pattern noise (known as fixed-pattern noise) and ambiguous object boundaries that hinder accurate segmentation. To address these challenges, this study presents a novel real-time semantic segmentation framework specifically designed for LWIR images. The framework incorporates a Stripe noise Denoising Module (SDM) and a Boundary Enhancement Module (BEM), which leverage frequency-domain filtering and learnable weights to adaptively process noisy input data and improve boundary prediction. In addition, a Multi-Stream Fusion Module (MSFM) integrates multi-scale semantic features with boundary information, thereby enhancing segmentation accuracy across diverse object scales. The proposed method demonstrates state-of-the-art performance in both accuracy and efficiency on multiple datasets, including KERInha and SODA. Extensive qualitative and quantitative evaluations further validate its robustness, particularly in scenarios where RGB imagery is unavailable. By eliminating the need for supplementary information such as depth data, this approach facilitates precise indoor and outdoor segmentation tasks with lightweight computation, making it highly suitable for real-world applications. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/saha3jet/pytorch\_efficientirseg</uri>.