Abstract:
Debris present inside water poses significant impacts on under water ecosystems along with living
organisms thriving in them. In order to address the issue of water pollution and environmental
hazards, detection and removal of this growing underwater debris is utmost need of the present
times. Therefore, this study explores the application of deep neural network model specifically
pretrained vgg16 for detection and classification of underwater debris. Additionally, the current
study presents a comprehensive locally collected under water debris images dataset for the
detection and classification of debris in local underwater environment is also proposed. The
proposed custom vgg16 model performs well in detection and classification of underwater debris
with an accuracy of 84%. Moreover, this model is effectively proficient in detecting plastic debris
present inside water environment. Furthermore, the model's strength was authenticated through
testing on unseen underwater debris images, showcasing its image detection potential for real
underwater ecosystem deployment. This study adds to the progression of automatic underwater
detection systems, proposing a viable tool for environmental mitigation.
