1. Joshua Myrans, Prof. Zoran Kapelan & Prof. Richard Everson Automating Detection of faults in small wastewater pipes, using CCTV footage Challenge Wastewater pipes inspected using CCTV −PIG (pipe inspection gadget) remotely controlled camera −Camera attached to a semi-rigid cable Expensive to collect and analyse −Analysed during the collection (slowing the collection process) −Manually analysed after collection (requiring all footage to be re- watched) Relies Heavily on the training and experience of the surveyor Aims -Develop new technology to detect and classify faults in wastewater pipes by automatically processing CCTV surveys -Test and validate the system in a real world environment -Create a flexible technology that can be applied to a wide range of wastewater pipes and faults -Produce a decision support tool to work alongside engineers to improve productivity Data −CCTV Footage provided by Wessex Water −Extracted a library of images from 5 surveys, covering 5.5km of 150-1800 mm sewers, of various materials. −Library contained 670 images, of which 312 with faults (described in Table 1). Table 1: describing the classifications of faults in the development library. Fault typeSubtypesPercentage (%) JointDisplaced, Open24 CrackLongitudinal, Circumferential, Multiple, Spiral 11 Broken / Collapsed-3 ObstaclesIntruding junctions, Masonry, Protrusions 15 Hole-11 DepositsAttached, Settled28 RootsFine, Tap, Mass3 InfiltrationRunning, Gushing1 BrickworkMissing mortar, Displaced bricks, Missing bricks 5 Methodology The developed methodology identifies an image as faulty or normal in 5 steps (show in Figure 1). 1.A frame is extracted from the video footage 2.Pre-processing prepares the frame, converting it to greyscale and segmenting the image 3.The prepared frame is then filtered in order to extract features (GIST features) 4.The filtered image is passed to a trained classifier (Support Vector Machine, Neural Network, Random Forest etc.) 5.The output of the classifier is interpreted, informing the user if the frame contains a fault. 2) Pre-processing1) Extract Frames 3) Apply Filters 4) Apply Classifier5) Identify Fault Figure 1: Flowchart demonstrating the key steps in the applied methodology Case Study Applying the defined methodology to the library of images detailed above an accuracy of 86% was achieved (FPR 8%, FNR 6%). −Classification was performed by a random forest classifier −Due to the random forest’s transparency it was possible to locate the faults in some frames (shown in figure 2.) −The threshold used to determine whether a frame contains a fault can be altered to improve performance as shown b the ROC curve in Figure 3. Displaced JointLarge Deposits Figure 2: Examples of frames where faults could be both identified and located Figure 3: ROC (receiver operating characteristic) curve, demonstrating how manipulating the classification threshold effects, true and false positive rates. ROC plot for the developed fault detection methodology Future Work −Improve detection accuracy, by fine tuning the classification technique −Classification of fault types −Further testing and validation on more CCTV footage Acknowledgements EPSRC WISE - EP/L016214/1