Sewer-AI: Sustainable Automated Analysis of Real-World Sewer Videos Using DNNs
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Automated maintenance of sewer networks using computer vision techniques has gained prominence in the vision-research community. In this work, we handle sewer inspection videos with severe challenges. These obstacles hinder direct application of state-of-the-art neural networks in finding a solution. Thus, we perform an exhaustive study on the performance of highly successful neural architectures on our challenging sewer-video-dataset. For complete understanding we analyze their performance in different modes. We propose training strategies for effectively handling the different challenges and obtain balanced accuracy, F1 and F2 scores of more than 90% for 17 out of the 25 defect categories. Furthermore, for developing resource efficient, sustainable versions of the models we study the trade-off between performance and parameter pruning. We show that the drop in average performance of the networks is within 1% with more than 90% weight pruning. We test our models on the state-of-the- art Sewer-ML-dataset and obtained 100% true positive rate for 8 out of 18 defect categories in the Sewer-ML-dataset.