Abstract
Underwater cables or pipelines are commonly utilized elements in ocean research, marine engineering, power transmission, and communication-based activities. Their performance necessitates regularly conducted inspection for maintenance purposes. A vision system is commonly used by autonomous underwater vehicles (AUVs) to track and search for underwater cable. Its traditional methods are characteristically applicable in AUVs, wherein they are equipped with handcrafted features and shallow trainable architectures. However, such methods are subpar or even incapable of tracking underwater cable in fast-changing and complex underwater conditions. In contrast to this, the deep learning method is linked with the capacity to learn semantic, high-level, and deeper features, thus rendering it recommended for performing underwater cable tracking. In this study, several deep Convolutional Neural Network (CNN) models were proposed to classify underwater cable images obtained from a set of underwater images, whereby transfer learning and data augmentation were applied to enhance the classification accuracy. Following a comparison and discussion regarding the performance of these models, MobileNetV2 outperformed among other models and yielded lower computational time and the highest accuracy for classifying underwater cable images at 93.5%. Hence, the main contribution of this study is geared toward developing a deep learning method for underwater cable image classification.
Highlights
Underwater infrastructures such as underwater communication cable, underwater power cable, and subsea pipeline are highly crucial to humankind
Deep Convolutional Neural Network (CNN) are one of the artificial neural networks (ANNs) formed by a stack of convolutional layers, activation function, pooling layers, and fully connected layers. Their natural procedure entails the learning of low-level and high-level features such as edges and curves, and shapes and different patterns from input image data, respectively [14]. Driven by such achievements across various research areas, this study employed the deep CNN method to perform the classification of underwater cable images
The optimization approach was applied to transfer data from the deep CNN model trained by the ImageNet database, which was retrained using the underwater cable images
Summary
Underwater infrastructures such as underwater communication cable, underwater power cable, and subsea pipeline are highly crucial to humankind. Some of the commonly implemented cable tracking methods include Hough Transform, Kalman filters, and particle filters, which incorporate an algorithm to search for the main straight line in the underwater images and reveal its position [2]. Traditional object detection models can roughly be categorized into three stages, namely the informative region selection, feature extraction, and classification [29] They are less effective in constructing a complex situation when classifying multiple low-level image features with high-level context [12]. The current study employs different types of deep CNN models to perform the classification of underwater cable images and varying optimization techniques are applied to increase their performance
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