TCLNet: A Thermal Contrast-Aware Lightweight Network for infrared object detection

Infrared object detection plays an important role in low-light and complex-environment perception. However, infrared images often exhibit low signal-to-noise ratio, weak target-background contrast, and limited texture information, which can degrade detection performance in small-target and cluttered-background scenarios while making it difficult to balance detection accuracy and model complexity. To address these challenges, this paper proposes TCLNet, a Thermal Contrast-Aware Lightweight Network for infrared object detection. TCLNet introduces a Thermal Contrast Encoder (TCE) to enhance local contrast-sensitive target representations and a Thermal-Guided Cross-Feature Attention (TGCA) mechanism to promote interactions between contrast-enhanced and semantic features, thereby improving target discrimination and suppressing background interference.Experiments were conducted independently on the M3FD, FLIR ADAS v2 (denoted as FLIR_2), and NUDT-SIRST1 datasets. TCLNet achieves mAP50 values of 60.5%, 45.4%, and 91.4%, respectively, corresponding to improvements of 12.8, 11.3, and 7.5 percentage points over the YOLO12n baseline. Meanwhile, the model maintains a compact size of approximately 2M parameters. These results demonstrate that TCLNet improves infrared target detection, particularly in low-SNR and small-target conditions, while achieving a favorable balance between detection accuracy and model complexity.