YOLOv7-tiny-CR: A Causal Intervention Framework for Infrared Small Target Detection with Feature Debiasing

The performance of infrared small target detection is often hindered by spurious correlations learned between features and labels. To address this feature bias at its root, this paper proposes a debiased detection framework grounded in causal reasoning. Built upon the YOLOv7-tiny architecture, the framework introduces a three-stage debiasing mechanism. First, a Structural Causal Model (SCM) is adopted to disentangle causal features from non-causal image cues. Second, a Causal Attention Mechanism (CAM) is embedded into the backbone, where a causality-guided feature weighting strategy enhances the model’s focus on semantically critical target characteristics. Finally, a Causal Intervention (CI) module is incorporated into the neck, leveraging backdoor adjustments to suppress spurious causal links induced by contextual confounders. Extensive experiments on the public FLIR_ADASv2 dataset demonstrate notable gains in feature discriminability, with improvements of 2.9% in mAP@50 and 2.7% in mAP@50:95 compared to the baseline. These results verify that the proposed framework effectively mitigates feature bias and enhances generalization capability, outperforming the baseline by a substantial margin.