TCLNet: A Thermal Contrast-Aware Lightweight Network for infrared object detection
- 1Xijing University, Xi'an, Shaanxi, China.
Abstract
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.
Related
- Subject-independent Parkinson's Disease Detection through A Lightweight Spectrotemporal Hybrid Network.
- Lightweight convolutional neural network for UAV onboard visual anomaly detection and localization
- Lightweight multi-task network for synchronous fatigue detection and ankle instability gait analysis in football training with edge deployment
- An energy-aware and lightweight spiking neural network framework for real-time intrusion detection in IoT networks
- WCCAN: Windowed Cross-contrast Attention Network for Multi-contrast Brain MR Image Super-resolution
BibTeX
@article{zhu2026tclnet,
title = {{TCLNet: A Thermal Contrast-Aware Lightweight Network for infrared object detection}},
author = {Xiyuan Zhu and Hongtao Dang and Xiaoyuan Jin},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0361114},
url = {https://doi.org/10.1371/journal.pone.0361114},
note = {CC BY},
}
RIS
TY - JOUR
TI - TCLNet: A Thermal Contrast-Aware Lightweight Network for infrared object detection
AU - Xiyuan Zhu
AU - Hongtao Dang
AU - Xiaoyuan Jin
PY - 2026
DA - 2026/10/22
JO - PLOS One
DO - 10.1371/journal.pone.0361114
UR - https://doi.org/10.1371/journal.pone.0361114
AB - 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.
SN - 1932-6203
ER -