Optimzied resnet model of convolutional neural network for under sea water object detection and classification

The world's ocean depths conceal a big mystery, and obtaining the information contained therein is a significant challenge that must be overcome. With the advent of computer vision technologies and robotics, the underwater environment is explored recently. The vast data collected from numerous underwater sensors have a variety of complications related to inadequate image quality, difficulty in acquiring training samples, and uncontrolled objects in the underwater environment. When these images are processed using machine learning techniques that involve manual intervention, the time taken to process a huge amount of images will be relatively high and prone to errors. To tackle these, we propose a novel hybrid capuchin-based coevolving particle swarm optimization (HC 2 PSO) algorithm with a ResNet model of Convolutional Neural Network (CNN) architecture for underwater object identification. This work mainly aims to explore different underwater objects such as fish, corals, sea urchins, etc. The speckle-reducing anisotropic diffusion (SRAD) filter performs the pre-processing step. The denoising autoencoder (DA) is used for feature extraction which can enhance the partially distorted sample images and offer increased robustness. To overcome the overfitting issue in CNN, the HC 2 PSO algorithm is used. The experimental works are handled in MATLAB software. Both with and without pre-processing results in terms of SRAD filter are checked and evaluated. The proposed method's effectiveness is evaluated through various measures like accuracy, specificity, sensitivity, false-positive rate, false-negative rates, etc. The accuracy of the HC 2 PSO-CNN classifier is higher when compared to the * V.