Revealing the Underlying Patterns: Investigating Dataset Similarity, Performance, and Generalization

Supervised deep learning models require significant amount of labelled data to achieve an acceptable performance on a specific task. However, when tested on unseen data, the models may not perform well. Therefore, the models need to be trained with additional and varying labelled data to improve the generalization. In this work, our goal is to understand the models, their performance and generalization. We establish image-image, dataset-dataset, and image-dataset distances to gain insights into the model's behavior. Our proposed distance metric when combined with model performance can help in selecting an appropriate model/architecture from a pool of candidate architectures. We have shown that the generalization of these models can be improved by only adding a small number of unseen images (say 1, 3 or 7) into the training set. Our proposed approach reduces training and annotation costs while providing an estimate of model performance on unseen data in dynamic environments.

TrueDeep: A systematic approach of crack detection with less data

Supervised and semi-supervised semantic segmentation algorithms require significant amount of annotated data to achieve a good performance. In many situations, the data is either not available or the annotation is expensive. The objective of this work is to show that by incorporating domain knowledge along with deep learning architectures, we can achieve similar performance with less data. We have used publicly available crack segmentation datasets and shown that selecting the input images using knowledge can significantly boost the performance of deep-learning based architectures. Our proposed approaches have many fold advantages such as low annotation and training cost, and less energy consumption. We have measured the performance of our algorithm quantitatively in terms of mean intersection over union (mIoU) and F score. Our algorithms, developed with 23% of the overall data; have a similar performance on the test data and significantly better performance on multiple blind datasets.

CoreDeep: Improving Crack Detection Algorithms Using Width Stochasticity

Automatically detecting or segmenting cracks in images can help in reducing the cost of maintenance or operations. Detecting, measuring and quantifying cracks for distress analysis in challenging background scenarios is a difficult task as there is no clear boundary that separates cracks from the background. Developed algorithms should handle the inherent challenges associated with data. Some of the perceptually noted challenges are color, intensity, depth, blur, motion-blur, orientation, different region of interest (ROI) for the defect, scale, illumination, complex and challenging background, etc. These variations occur across (crack inter class) and within images (crack intra-class variabilities). Overall, there is significant background (inter) and foreground (intra-class) variability. In this work, we have attempted to reduce the effect of these variations in challenging background scenarios. We have proposed a stochastic width (SW) approach to reduce the effect of these variations. Our proposed approach improves detectability and significantly reduces false positives and negatives. We have measured the performance of our algorithm objectively in terms of mean IoU, false positives and negatives and subjectively in terms of perceptual quality.