To address the challenges of low accuracy and high model complexity in tunnel blast hole recognition caused by poor lighting and high dust levels, this study proposes a lightweight blast hole detection algorithm that integrates infrared thermal imaging with deep learning technology. Using YOLOv11n as the baseline model, the lightweight StarNet network is first incorporated to enhance multi-scale feature extraction capabilities and optimize computational efficiency. Subsequently, the Star Block module is introduced to construct the C3k2_BlockStar module, improving the fusion of features at different scales. Finally, a lightweight detection head is designed to further reduce computational complexity. Experimental results demonstrate that the improved model reduces the number of parameters by 54% and the model size to 32% of the original, achieving effective recognition of tunnel infrared blast holes. This research provides a technical foundation for robotic automated charging in tunnel construction, offering significant practical value and application prospects.