Accelerating multi-class defect detection of building façades using knowledge distillation of DCNN-based model

초록

This paper proposes a high-speed detection method for multi-class defects in residential building façades. Automated deep learning-based defect detection systems have been developed to compensate for various problems in existing human-oriented defect management methods for building façades. However, the superior performance of deep learning-based models occasionally causes a trade-off with the inference time. In other words, using a lightweight model results in performance degradation, which we propose to prevent through a knowledge distillation (KD) method. This study was conducted using approximately 10,000 building façade images, which were obtained using drones. Using these data, we compared the performances of the lightweight model trained simply and the model trained with a KD method. As a result, mean average precision (mAP) increased by approximately 20% and inference time decreased by approximately 2.5x. © International Journal of Sustainable Building Technology and Urban Development.

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