  Vision-based crack detection faces deployment challenges due to the size of
robust models and edge device limitations. These can be addressed with
lightweight models trained with knowledge distillation (KD). However,
state-of-the-art (SOTA) KD methods compromise anti-noise robustness. This paper
develops Robust Feature Knowledge Distillation (RFKD), a framework to improve
robustness while retaining the precision of light models for crack
segmentation. RFKD distils knowledge from a teacher model's logit layers and
intermediate feature maps while leveraging mixed clean and noisy images to
transfer robust patterns to the student model, improving its precision,
generalisation, and anti-noise performance. To validate the proposed RFKD, a
lightweight crack segmentation model, PoolingCrack Tiny (PCT), with only 0.5 M
parameters, is also designed and used as the student to run the framework. The
results show a significant enhancement in noisy images, with RFKD reaching a
62% enhanced mean Dice score (mDS) compared to SOTA KD methods.
