Deep Learning-Based Detection of Pipes in Industrial Environments

Abstract

Robust perception is generally produced through complex multimodal perception pipelines, but these kinds of methods are unsuitable for autonomous UAV deployment, given the restriction found on the platforms. This chapter describes developments and experimental results produced to develop new deep learning (DL) solutions for industrial perception problems. An earlier solution combining camera, LiDAR, GPS, and IMU sensors to produce high rate, accurate, robust detection, and positioning of pipes in industrial environments is to be replaced by a single camera computationally lightweight convolutional neural network (CNN) perception technique. In order to develop DL solutions, large image datasets with ground truth labels are required, so the previous multimodal technique is modified to be used to capture and label datasets. The labeling method developed automatically computes the labels when possible for the images captured with the UAV platform. To validate the automated dataset generator, a dataset is produced and used to train a lightweight AlexNet-based full convolutional network (FCN). To produce a comparison point, a weakened version of the multimodal approach-without using prior data-is evaluated with the same DL-based metrics.

Real-Time Pipe and Valve Characterisation and Mapping for Autonomous Underwater Intervention Tasks

“…The metrics presented in this section outperform other state-of-the-art methods for pipe recognition: [ 35 ]-traditional computer vision algorithms over 2D underwater images achieving an F1-score of 94.1%, [ 7 ]-traditional computer vision algorithms over 2D underwater images achieving a mean F1-score over three datasets of 88.0% and [ 45 ]-deep learning approach for 2D drone imagery achieving a pixel-wise accuracy of 73.1%.…”

“…Few research studies involving pipelines are restricted to damage evaluation [ 42 , 43 ] or pipeline navigation from the inside [ 44 ]. Guerra et al in [ 45 ] present one of the most advanced works on pipeline recognition using deep learning, where a drone equipped with a monocular camera is used to perform 2D detection of pipelines in industrial environments.…”

Localization, Mapping and SLAM in Marine and Underwater Environments

“…Looking at the metrics presented by the best performing experiment for each class, it can be seen that the Pipe class achieved an F1-score of 97.1%, outperforming other stateof-the-art methods for underwater pipe segmentation: [35]-traditional computer vision algorithms over 2D underwater images achieving an F1-score of 94.1%, [7]-traditional computer vision algorithms over 2D underwater images achieving a mean F1-score over three datasets of 88.0% and [45]-deep leaning approach for 2D drone imagery achieving a pixel-wise accuracy of 73.1%. For the valve class, the BS 1_075 experiment achieved a F1-score of 94.9%, being a more challenging class due to its complex geometry.…”

“…Few research studies involving pipelines are restricted to damage evaluation [42,43] or valve detection for navigation [44] working with images taken from inside the pipelines. The only known work addressing pipeline recognition using deep learning is from Guerra et al in [45], where a camera-equipped drone is used to detect pipelines in industrial environments.…”

“…Although this is out of the scope of this paper, this vision-based algorithm can complement the navigation facilities of underwater vehicles equipped with multiple types of sensors. In fact, this algorithm can integrate additional sensorial data in the first estimation of the vehicle motion, combining visual odometry with other means of laser or sonar-based dead reckoning [44,45].…”