Toward a Highly Accurate Classification of Underwater Cable Images via Deep Convolutional Neural Network

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.