Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Monocular Depth Estimation using a Multi-grid Attention-based Model
Volume-4 | Issue-3
Speedy Image Crowd Counting by Light Weight Convolutional Neural Network
Volume-3 | Issue-3
Construction of Efficient Smart Voting Machine with Liveness Detection Module
Volume-3 | Issue-3
An Economical Robotic Arm for Playing Chess Using Visual Servoing
Volume-2 | Issue-3
Triplet loss for Chromosome Classification
Volume-4 | Issue-1
Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm
Volume-3 | Issue-4
Real Time Sign Language Recognition and Speech Generation
Volume-2 | Issue-2
Analysis of Artificial Intelligence based Image Classification Techniques
Volume-2 | Issue-1
Design of ANN Based Machine Learning Method for Crop Prediction
Volume-3 | Issue-3
A REVIEW ON IOT BASED MEDICAL IMAGING TECHNOLOGY FOR HEALTHCARE APPLICATIONS
Volume-1 | Issue-1
COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT
Volume-1 | Issue-1
Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Accurate Segmentation for Low Resolution Satellite images by Discriminative Generative Adversarial Network for Identifying Agriculture Fields
Volume-3 | Issue-4
Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization
Volume-3 | Issue-4
State of Art Survey on Plant Leaf Disease Detection
Volume-4 | Issue-2
Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data
Volume-3 | Issue-4
OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry
Volume-3 | Issue-4
VIRTUAL RESTORATION OF DAMAGED ARCHEOLOGICAL ARTIFACTS OBTAINED FROM EXPEDITIONS USING 3D VISUALIZATION
Volume-1 | Issue-2
Two-Stage Frame Extraction in Video Analysis for Accurate Prediction of Object Tracking by Improved Deep Learning
Volume-3 | Issue-4
Volume - 6 | Issue - 1 | march 2024
Published
25 April, 2024
Underwater environments present unique challenges for imaging due to factors such as light attenuation, scattering, and colour distortion. This research combines advanced CNN models like CBAM(convolutional Block Attention Mod-ule) and VGG16 with state-of-the-art object detection methods of CNN like YOLO or RCNN to enhance the visual quality of underwater images and to detect the objects based on an accuracy rate. Leveraging the various capabilities of the VGG16 model, pretrained on extensive datasets, the system efficiently restores degraded underwater images by capturing and learning intricate features. Integrating the CBAM model enhances this process by selectively attending to salient features while suppressing irrelevant ones, thereby refining the restoration results. Additionally, the combined architecture facilitates object detection within the restored images, enabling the identification and localization of submerged objects with high accuracy. Currently the work presents short review on the existing methods of underwater image restoration and a suggests method employing the CBAM(convolutional Block Attention Mod-ule) and VGG16 to overcome the prevailing challenges in underwater object detection. In future, the research aims to present a website that would be more useful for the students , researchers and the underwater explorers.
KeywordsConvolutional Neural Networks (CNNs) VGG 16 Underwater Object Detection Image Restoration
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