Undersea Water Objects Detection and Classification Using Optimized Region-Based Convolutional Neural Network

Underwater autonomous operation is becoming increasingly crucial to avoid the hazardous in the environment of high-pressure deep-sea due to the significance of underwater investigation. The most crucial piece of technology for underwater-based task is intelligent computer vision. In an underwater environment, underwater vision requires good image quality, and illumination with better classification of sea objects. This work presented a novel technique of Optimized Region-based Convolutional Neural Network (ORCNN). For this work, the Gaussian filter is used to remove the noise and enhance the image quality during pre-processing and the Improved Affinity Propagation Clustering (IAPC) model segments the objects. After that, the Region-based Convolutional Neural Network (RCNN) model classifies various objects such as urchins, seagrass, fishes, and rocks in which the RCNN parameters are tuned via a light spectrum optimizer algorithm (LSOA). can also input the segmentation prediction images and the labeled images into the discriminant convolutional network and improve the segmentation accuracy of underwater images by further enhancing the essential characteristics of learning data through the confrontation training of generators and discriminators. The experimental results demonstrate that higher performance is provided by the newly developed ORCNN -Net predictive model when compared to other comparative algorithms while considering the negative and positive metrics

From Resilience-Building to Resilience-Scaling Technologies: Directions -- ReSIST NoE Deliverable D13

This document is the second product of workpackage WP2, "Resilience-building and -scaling technologies", in the programme of jointly executed research (JER) of the ReSIST Network of Excellence. The problem that ReSIST addresses is achieving sufficient resilience in the immense systems of ever evolving networks of computers and mobile devices, tightly integrated with human organisations and other technology, that are increasingly becoming a critical part of the information infrastructure of our society. This second deliverable D13 provides a detailed list of research gaps identified by experts from the four working groups related to assessability, evolvability, usability and diversit