Deep learning-based marine big data fusion for ocean environment monitoring: Towards shape optimization and salient objects detection

Abstract

Objective: During the last few years, underwater object detection and marine resource utilization have gained significant attention from researchers and become active research hotspots in underwater image processing and analysis domains. This research study presents a data fusion-based method for underwater salient object detection and ocean environment monitoring by utilizing a deep model. Methodology: A hybrid model consists of an upgraded AlexNet with Inception v-4 for salient object detection and ocean environment monitoring. For the categorization of spatial data, AlexNet is utilized, whereas Inception V-4 is employed for temporal data (environment monitoring). Moreover, we used preprocessing techniques before the classification task for underwater image enhancement, segmentation, noise and fog removal, restoration, and color constancy. Conclusion: The Real-Time Underwater Image Enhancement (RUIE) dataset and the Marine Underwater Environment Database (MUED) dataset are used in this research project’s data fusion and experimental activities, respectively. Root mean square error (RMSE), computing usage, and accuracy are used to construct the model’s simulation results. The suggested model’s relevance form optimization and conspicuous item prediction issues in the seas is illustrated by the greatest accuracy of 95.7% and low RMSE value of 49 when compared to other baseline models.