Deep Learning Approach For Objects Detection in Underwater Pipeline Images

In this paper, we present automatic, deep-learning methods for pipeline detection in underwater environments. Seafloor pipelines are critical infrastructure for oil and gas transport. The inspection of those pipelines is required to verify their integrity and determine the need for maintenance. Unde...

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Bibliographic Details
Published in:Applied artificial intelligence 2022-12, Vol.36 (1)
Main Authors: Gašparović, Boris, Lerga, Jonatan, Mauša, Goran, Ivašić-Kos, Marina
Format: Article
Language:English
Subjects:
Artificial neural networks
Datasets
Deep learning
Detectors
Frames per second
Gas pipelines
Image quality
Inspection
Light refraction
Literature reviews
Machine learning
Object recognition
Ocean floor
Petroleum pipelines
Underwater pipelines
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Summary:In this paper, we present automatic, deep-learning methods for pipeline detection in underwater environments. Seafloor pipelines are critical infrastructure for oil and gas transport. The inspection of those pipelines is required to verify their integrity and determine the need for maintenance. Underwater conditions present a harsh environment that is challenging for image recognition due to light refraction and absorption, poor visibility, scattering, and attenuation, often causing poor image quality. Modern machine-learning object detectors utilize Convolutional Neural Network (CNN), requiring a training dataset of sufficient quality. In the paper, six different deep-learning CNN detectors for underwater object detection were trained and tested: five are based on the You Only Look Once (YOLO) architectures (YOLOv4, YOLOv4-Tiny, CSP-YOLOv4, YOLOv4@Resnet, YOLOv4@DenseNet), and one on the Faster Region-based CNN (RCNN) architecture. The models' performances were evaluated in terms of detection accuracy, mean average precision (mAP), and processing speed measured with the Frames Per Second (FPS) on a custom dataset containing underwater pipeline images. In the study, the YOLOv4 outperformed other models for underwater pipeline object detection resulting in an mAP of 94.21% with the ability to detect objects in real-time. Based on the literature review, this is one of the pioneering works in this field.
ISSN:0883-9514
1087-6545
DOI:10.1080/08839514.2022.2146853