Authors: Engels, J; Gonzalez, H; Aedo, G

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Engels, J, Gonzalez, H & Aedo, G 2019, 'Applying image classification to develop artificial intelligence for tailings storage facility hazard monitoring using site-based cameras', in AJC Paterson, AB Fourie & D Reid (eds), Proceedings of the 22nd International Conference on Paste, Thickened and Filtered Tailings, Australian Centre for Geomechanics, Perth, pp. 197-204.

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Abstract:
Image classification is a process whereby the spectral information of an image, based on its digital numbers, attempts to classify individual pixels to a theme or specific object (e.g. vegetation, water, vehicles, people, etc.). The output is generally an image map or mosaic of pixels, each of which belong to a particular theme or identification to produce an independent overlay of the original image. This overlay can be used to provide a post analysis regarding changes that are occurring in a sequence of images or, for example, identify a potential hazard that can trigger an action for human intervention. The accuracy of image classification is based on having enough information to train a model to identify the theme or object of interest. This paper presents the results of a supervised machine learning technique whereby target objects were identified and models run to train the classification algorithm to identify changes in supernatant pond size, rates of rise, detection of inflows of water to an area and presence of mobile equipment. Training images were acquired from site-based static time-lapse cameras that have been taking images since early 2017 of different areas of a tailings storage facility in the north of Chile.

Keywords: cameras, monitoring, machine learning, artificial intelligence

References:
Engels, J & Vega, F 2016, ‘Remote sensing techniques to characterize tailings deposition behaviour to improve daily planning and provide early warning systems’, in S Barrera & RJ Jewell (eds), Proceedings of the 19th International Seminar on Paste and Thickened Tailings, Gecamin, Santiago.
Geitgey, A 2016, Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks, Medium, viewed 18 August 2018,
Google Inc. 2011, TensorFlow, computer software, Google Inc., Menlo Park, https://www.tensorflow.org/
He, K, Gkioxari, G, Dollár, P & Girshick, R 2017, ‘Mask R-CONN’, Proceedings of the 2017 IEEE International Conference on Computer Vision, IEEE Computer Society, Piscataway, pp. 2980-2988.




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