Autoencoders for Semi-Supervised Water Level Modeling in Sewer Pipes with Sparse Labeled Data

More frequent and thorough inspection of sewer pipes has the potential to save billions in utilities. However, the amount and quality of inspection are impeded by an imprecise and highly subjective manual process. It involves technicians judging stretches of sewer based on video from remote-controlled robots. Determining the state of sewer pipes based on these videos entails a great deal of ambiguity. Furthermore, the frequency with which the different defects occur differs a lot, leading to highly imbalanced datasets. Such datasets represent a poor basis for automating the labeling process using supervised learning. With this paper we explore the potential of self-supervision as a method for reducing the need for large numbers of well-balanced labels. First, our models learn to represent the data distribution using more than a million unlabeled images, then a small number of labeled examples are used to learn a mapping from the learned representations to a relevant target variable, in this case, water level. We choose a convolutional Autoencoder, a Variational Autoencoder and a Vector-Quantised Variational Autoencoder as the basis for our experiments. The best representations are shown to be learned by the classic Autoencoder with the Multi-Layer Perceptron achieving a Mean Absolute Error of 9.93. This is an improvement of 9.62 over the fully supervised baseline.Peer ReviewedPostprint (published version

Dual-Stream Attention Transformers for Sewer Defect Classification

We propose a dual-stream multi-scale vision transformer (DS-MSHViT) architecture that processes RGB and optical flow inputs for efficient sewer defect classification. Unlike existing methods that combine the predictions of two separate networks trained on each modality, we jointly train a single network with two branches for RGB and motion. Our key idea is to use self-attention regularization to harness the complementary strengths of the RGB and motion streams. The motion stream alone struggles to generate accurate attention maps, as motion images lack the rich visual features present in RGB images. To facilitate this, we introduce an attention consistency loss between the dual streams. By leveraging motion cues through a self-attention regularizer, we align and enhance RGB attention maps, enabling the network to concentrate on pertinent input regions. We evaluate our data on a public dataset as well as cross-validate our model performance in a novel dataset. Our method outperforms existing models that utilize either convolutional neural networks (CNNs) or multi-scale hybrid vision transformers (MSHViTs) without employing attention regularization between the two streams

The role of deep learning in urban water management: A critical review

This is the final version. Available on open access from Elsevier via the DOI in this recordDeep learning techniques and algorithms are emerging as a disruptive technology with the potential to transform global economies, environments and societies. They have been applied to planning and management problems of urban water systems in general, however, there is lack of a systematic review of the current state of deep learning applications and an examination of potential directions where deep learning can contribute to solving urban water challenges. Here we provide such a review, covering water demand forecasting, leakage and contamination detection, sewer defect assessment, wastewater system state prediction, asset monitoring and urban flooding. We find that the application of deep learning techniques is still at an early stage as most studies used benchmark networks, synthetic data, laboratory or pilot systems to test the performance of deep learning methods with no practical adoption reported. Leakage detection is perhaps at the forefront of receiving practical implementation into day-to-day operation and management of urban water systems, compared with other problems reviewed. Five research challenges, i.e., data privacy, algorithmic development, explainability and trustworthiness, multi-agent systems and digital twins, are identified as key areas to advance the application and implementation of deep learning in urban water management. Future research and application of deep learning systems are expected to drive urban water systems towards high intelligence and autonomy. We hope this review will inspire research and development that can harness the power of deep learning to help achieve sustainable water management and digitalise the water sector across the world.Royal SocietyAlan Turing InstituteNational Natural Science Foundation of Chin

Deep learning based defect detection algorithm for solar panels

Defect detection of solar panels plays an essential role in guaranteeing product quality within automated production lines. However, traditional manual inspection of solar panel defects suffers from low efficiency. This paper proposes an enhanced YOLOv5 algorithm (EL-YOLOv5) fused with the CBAM hybrid attention module to ensure product quality. The algorithm focuses on detecting five common types of defects that frequently appear on photovoltaic production lines, namely hidden cracks, scratches, broken grids, black spots, and short circuits. This study utilizes publicly available solar panel datasets, as well as datasets collected from actual photovoltaic production lines. These datasets are annotated accordingly and used to train the proposed algorithm. The experimental results demonstrate that the proposed algorithm achieves good performance on both the public and actual solar panel defect datasets. Particularly in actual datasets, where defect features are often less apparent and defects are smaller in size, the proposed algorithm can still detect even minor black spots

Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark

Perhaps surprisingly sewerage infrastructure is one of the most costly infrastructures in modern society. Sewer pipes are manually inspected to determine whether the pipes are defective. However, this process is limited by the number of qualified inspectors and the time it takes to inspect a pipe. Automatization of this process is therefore of high interest. So far, the success of computer vision approaches for sewer defect classification has been limited when compared to the success in other fields mainly due to the lack of public datasets. To this end, in this work we present a large novel and publicly available multi-label classification dataset for image-based sewer defect classification called Sewer-ML. The Sewer-ML dataset consists of 1.3 million images annotated by professional sewer inspectors from three different utility companies across nine years. Together with the dataset, we also present a benchmark algorithm and a novel metric for assessing performance. The benchmark algorithm is a result of evaluating 12 state-of-the-art algorithms, six from the sewer defect classification domain and six from the multi-label classification domain, and combining the best performing algorithms. The novel metric is a class-importance weighted F2 score, $\text{F}2_{\text{CIW}}$, reflecting the economic impact of each class, used together with the normal pipe F1 score, $\text{F}1_{\text{Normal}}$. The benchmark algorithm achieves an $\text{F}2_{\text{CIW}}$ score of 55.11% and $\text{F}1_{\text{Normal}}$ score of 90.94%, leaving ample room for improvement on the Sewer-ML dataset. The code, models, and dataset are available at the project page https://vap.aau.dk/sewer-ml/Comment: CVPR 2021. Project webpage: https://vap.aau.dk/sewer-ml