A Lightweight Infrared Thermal Image Recognition Network with Thermal Noise Suppression and Dynamic Feature Fusion for Transformer Oil Leakage Detection

Detecting transformer oil leakage from infrared thermal images is challenging due to complex thermal backgrounds, low contrast, and sensor noise. To address these issues, this study proposes IR-OilNet, a lightweight CNN–Transformer fusion network designed for real-time infrared leakage detection in inspection robots. This research contributes to SDG 6 by preventing industrial water pollution resulting from transformer oil runoff, thereby protecting vital water sources in remote environments. The proposed framework integrates three key components: (1) a parameter-free Thermal Noise Suppression (TNS) module that reduces high-frequency thermal noise while preserving structural information; (2) a compact CNN backbone for local thermal texture extraction; and (3) a lightweight Transformer branch for global contextual modeling. To further enhance feature integration, a Dynamic Weight Fusion (DWF) module is introduced to adaptively balance local and global representations. Experiments on an expanded dataset of 2700 infrared thermal images demonstrate that IR-OilNet achieves an accuracy of 0.9992, F1-score of 0.9992, and AUC of 0.9993, while maintaining only 0.20 M parameters and 0.19 GFLOPs with real-time inference at 433 FPS. Compared with representative CNN and Transformer models, the proposed method achieves a superior trade-off between accuracy and computational efficiency, making it suitable for deployment in resource-constrained inspection systems.