A Novel Deep Learning Model for Sea State Classification Using Visual-Range Sea Images

• Published in: Symmetry (Basel) Vol. 14; no. 7; p. 1487
• Main Authors: Umair, Muhammad, Hashmani, Manzoor Ahmed, Hussain Rizvi, Syed Sajjad, Taib, Hasmi, Abdullah, Mohd Nasir, Memon, Mehak Maqbool
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2022
• Subjects: Accuracy; Asymmetry; Capital expenditures; Classification; Datasets; Deep learning; Expenditures; Image classification; Machine learning; Offshore platforms; Offshore structures; Satellites; sea state classification; Sea states; Slopes; Statistical methods; Structural safety; Tourism; visual-range dataset; Wave crest; Wind waves
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym14071487

Wind-waves exhibit variations both in shape and steepness, and their asymmetrical nature is a well-known feature. One of the important characteristics of the sea surface is the front-back asymmetry of wind-wave crests. The wind-wave conditions on the surface of the sea constitute a sea state, which is listed as an essential climate variable by the Global Climate Observing System and is considered a critical factor for structural safety and optimal operations of offshore oil and gas platforms. Methods such as statistical representations of sensor-based wave parameters observations and numerical modeling are used to classify sea states. However, for offshore structures such as oil and gas platforms, these methods induce high capital expenditures (CAPEX) and operating expenses (OPEX), along with extensive computational power and time requirements. To address this issue, in this paper, we propose a novel, low-cost deep learning-based sea state classification model using visual-range sea images. Firstly, a novel visual-range sea state image dataset was designed and developed for this purpose. The dataset consists of 100,800 images covering four sea states. The dataset was then benchmarked on state-of-the-art deep learning image classification models. The highest classification accuracy of 81.8% was yielded by NASNet-Mobile. Secondly, a novel sea state classification model was proposed. The model took design inspiration from GoogLeNet, which was identified as the optimal reference model for sea state classification. Systematic changes in GoogLeNet’s inception block were proposed, which resulted in an 8.5% overall classification accuracy improvement in comparison with NASNet-Mobile and a 7% improvement from the reference model (i.e., GoogLeNet). Additionally, the proposed model took 26% less training time, and its per-image classification time remains competitive.