This paper reviews the state of the art in sensors and automated inspection devices for enhanced sewer inspection. Efficiency, safety, environmental, and legislative concerns have made inspection and assessment of communal sewers a central issue to water and sewerage companies. Nowadays, the standard sewer inspection system is based on a wheeled platform on which a closed circuit television (CCTV) camera is mounted. One of the disadvantages of camera inspection systems is that they can only detect a small proportion of all possible damage in a sewer. The inspection outcome of such systems relies not only on the quality of the acquired images, but also on the off-line. recognition and classification conducted by human operators. In consequence, CCTV-based platforms are frequently not effective. Infrared, microwave, optical, and ultrasonic-based sensors have been proposed to complement the existing CCTV-based approach and to improve inspection results. New inspection devices employing multiple sensors and being capable of carrying out remote sewer inspection tasks are under research.

Closed-circuit television (CCTV) is currently used in many inspection applications, such as the inspection of nonaccessible pipe surfaces. This human-oriented approach based on offline analysis of the raw images is highly subjective and prone to error because of the exorbitant amount of data to be assessed. Laser profilers have been recently proposed to project well-defined light patterns, improving the illumination of standard CCTV systems as well as enhancing the capability of automating the assessment process. This research shows that positional (geometrical) as well as intensity information, related to potential defects, can be extracted from the acquired laser projections. While most researchers focus on the analysis of positional information obtained from the acquired profiler signals, here the intensity information contained within the reflected light is also exploited for the purpose of defect classification and visualization. This paper describes novel strategies created for the automation of defect classification in tubular structures and explores new methods to fuse intensity and positional information, achieving improved multivariable defect classification. The acquired camera/laser images are processed in order to extract signal information for the purpose of visualization and map creation for further assessment. Then, a two-stage approach based on image processing and artificial neural networks is used to classify the images. First, a binary classifier identifies defective pipe sections, and then in a second stage, the defects are classified into different types, such as holes, cracks, and protruding obstacles. Experimental results are provided. Note to Practitioners-The method presented in this paper aims to automate the inspection of nonaccessible pipe surfaces. The method was thought to be employed in the inspection of sewers; however, it could be used in many other industrial applications and could also be extended to other shapes rather than tubular structures. A laser ring profiler, consisting, for instance, of a laser diode and a ring projector, can be easily integrated into existing closed-circuit television systems. The proposed algorithm identifies defective areas and categorizes the types of defects, analyzing the successive recorded camera images that will contain the reflected ring of light. The algorithm, that can be used online, makes use of the deformation of the reflected laser ring together with its changes in intensity. The fact of combining the two kinds of data using artificial-intelligent algorithms makes the method robust enough to work in harsh environments

This paper presents a new sensing methodology for the automated inspection of pipes. Standard inspection systems, as they are for example used in waste pipes and drains, are based on closed-circuit television cameras which are mounted on remotely controlled platforms and connected to remote video recording facilities. Two of the main disadvantages of such camera-based inspection systems are: 1) the poor quality of the acquired images due to difficult lighting conditions and 2) the susceptibility to error during the offline video assessment conducted by human operators. The objective of this research is to overcome these disadvantages and to create an intelligent sensing approach for improved and automated pipe-condition assessment. This approach makes use of a low-cost lighting profiler and a camera which acquires images of the light projections on the pipe wall. A novel method for extracting and analyzing intensity variations in the acquired images is introduced. The image data analysis is based on differential processing leading to highly-noise tolerant algorithms, particularly well suited for the detection of small faults in harsh environments. With the subsequent application of artificial neural networks, the system is capable of recognizing defective areas with a high success rate. Experiments in a range of waste pipes with different diameters and material properties have been conducted and test results are presented.

Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1) The mixing process should occur at macroscopic level and (2) Photons must interact with single material before reaching the sensor. However, these assumptions do not always hold and more complex nonlinear models are required. This study proposes a new hybrid method for switching between linear and nonlinear spectral unmixing of hyperspectral data based on artificial neural networks. The neural networks was trained with parameters within a window of the pixel under consideration. These parameters are computed to represent the diversity of the neighboring pixels and are based on the Spectral Angular Distance, Covariance and a non linearity parameter. The endmembers were extracted using Vertex Component Analysis while the abundances were estimated using the method identified by the neural networks (Vertex Component Analysis, Fully Constraint Least Square Method, Polynomial Post Nonlinear Mixing Model or Generalized Bilinear Model). Results show that the hybrid method performs better than each of the individual techniques with high overall accuracy, while the abundance estimation error is significantly lower than that obtained using the individual methods. Experiments on both synthetic dataset and real hyperspectral images demonstrated that the proposed hybrid switch method is efficient for solving spectral unmixing of hyperspectral images as compared to individual algorithms.

We propose a low false alarm methodology to determine anomalies in hyperspectral data. The method is based on the assumptions that the linear mixing model is valid and that, due to the resolution of the image, most pixels are mixtures of common substances, of which pure pixels (not mixtures) are rare. In the first stage of the algorithm, the classes associated with the background, which are the dominant classes in the image, are found by clustering the image pixels. The resulting clusters may be considered as representatives of the background classes in the image. In order to determine the anomalous pixels, a threshold may be applied to the distance between the pixel spectrum and the cluster centers. However, pixels corresponding to anomalies and pure substances will both show high distances. If we consider that the background classes are themselves most likely mixtures of other materials, the pixels within the convex hull formed by the background classes will have positive fractions that are smaller than one. The pure substances, however, will be outside such a convex hull and will show negative or superunity fractions. Pixels with such mixing proportions are explained as linear combinations of the background classes and, therefore, as not true anomalies. Pixels corresponding to anomalies, however, when expressed as linear combinations of the background classes, show high residual error even with negative and superunity mixing proportions. We use the unmixing spectral linear model without the nonnegativity constraint to distinguish between false anomalies corresponding to pure substances and real man-made anomalies.