Implicit Gradient-Modulated Semantic Data Augmentation for Deep Crack Recognition

Crack detection has attracted extensive attention in an intelligent transportation system (ITS). Despite the substantial progress of deep learning technology on crack recognition tasks, due to the various limitations in traffic, equipment, and time, it is hard to collect copious samples for training deep models. Considering this, implicitly semantic data augmentation (ISDA) tries to augment the training set in the feature space. However, when applying it to crack recognition tasks, our empirical studies reveal that those poor-classified augmented samples have little semantic relevance to the crack class, resulting in a non-negligible negative effect on training deep models. Since the augmented features follow the multivariate normal distribution, it is computationally inefficient to explicitly sample those features and filter out the hard-classified augmented features. To this end, we propose the implicit gradient-modulated semantic data augmentation (IGMSDA) for addressing the above problems. Concretely, this paper first proposes gradient-modulated (GM) loss to dynamically modulate the gradient of those poor-classified augmented samples by reshaping the standard cross-entropy loss. And then, in the feature space, we derive an upper bound of the expected GM loss on the augmented training set to avoid the costly explicit sampling process. Experiments show that IGMSDA improves the generalization performance of the existing deep models on crack recognition datasets.