A Deep Learning-Based Approach to Failure Detection in Mooring (Thin) Lines from Marine Images

Abstract

Mooring systems are incorporated from mooring (thin) lines that are constituted of fiber ropes, steel wires, and chains. Mooring systems are used for the stationary keeping of floating units during the drilling process of oil and gas from offshore deep water, and the unloading of productions to the shuttle storage tanker. However, it is crucial to monitor mooring systems for early-stage failure detection in mooring lines during offshore mooring operations to avoid any unexpected losses, including human injuries, and catastrophic failure. This paper addresses the challenges of mooring line detection, and proposes a deep learning-based approach for the detection of mooring lines from marine images using the bounding box. A convolutional neural network, Inception v3, is used for the detection and classification of thin-line objects from marine images, and it is a pre-trained model with 1000 classes. Furthermore, various testing samples have been evaluated for assessing the performance of the pre-trained proposed model. According to the results, it has been observed that the proposed model obtained the highest accuracy (87.33%) in classifying the mooring line objects from images, but failed to accurately detect mooring lines. Furthermore, in a few highlighted cases, the performance of the model decreased in terms of accuracy due to the misclassification and wrong detection of mooring line objects. Despite this, the proposed study furnishes a potential solution for the detection of failure in mooring lines from marine images.

Keywords

• mooring lines
• mooring systems
• failure detection
• thin line detection
• deep learning
• Inception v3