We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Aquatic Trash Detection and Classification: a Machine Learning and Deep Learning Perspective
Summary
This review examines machine learning and deep learning approaches for detecting and classifying aquatic trash in waterways, evaluating how computer vision algorithms trained on underwater and surface imagery can automate pollution monitoring for faster, more scalable ocean cleanup.
The escalating volume of pollutants flowing into the oceans and waterways is an alarming concern, not only to marine ecosystems but also to the health and livelihoods of communities worldwide. The rate at which aquatic trash is accumulating far outpaces its’ slow degradation, creating a persistent and growing problem. Both prevention and cleanup are essential for restoring and maintaining healthy aquatic environments. Advanced technology combining machine learning and deep learning algorithms with autonomous underwater vehicles (AUVs) is creating intelligent, automated solutions for detecting and removing trash from the waterways. This approach simplifies the cleanup process and is more efficient than manual methods. This paper examines the crucial role of machine learning and deep learning in detecting various types of aquatic trash. It offers a comprehensive analysis of recent research in the field, comparing different studies based on a variety of parameters. The study also discusses the challenges of trash detection in dynamic aquatic environments, highlighting scope for the future research
Sign in to start a discussion.
More Papers Like This
Detection of Trash in Sea Using Deep Learning
Researchers developed a deep learning convolutional neural network (CNN) model to detect and classify trash in marine and aquatic environments from underwater images, aiming to overcome the limitations of manual debris detection for objects that may be submerged or partially obscured.
A Comprehensive Review of Deep Learning Algorithms for Underwater Trash Detection: Advancements, Challenges, and Future Directions
This review examines deep learning approaches for automated underwater trash detection, covering CNN-based architectures including YOLO and Faster R-CNN, and finds they outperform traditional sonar and manual inspection methods while identifying key challenges such as low visibility and limited labeled datasets.
A Comprehensive Review of Deep Learning Algorithms for Underwater Trash Detection: Advancements, Challenges, and Future Directions
This review examines deep learning approaches for automated underwater trash detection, covering CNN-based architectures including YOLO and Faster R-CNN, and finds they outperform traditional sonar and manual inspection methods while identifying key challenges such as low visibility and limited labeled datasets.
Underwater Image Detection for Cleaning Purposes; Techniques Used for Detection Based on Machine Learning
Researchers reviewed machine learning techniques for underwater image detection to support water pollution cleanup, focusing on convolutional neural networks and region-based CNN methods for identifying surface mucilage and debris. The study evaluated supervised classification algorithms as the most effective approach for automated aquatic waste detection systems.
Deep-Feature-Based Approach to Marine Debris Classification
This study applied deep learning to classify marine debris from images, demonstrating that feature-based neural network approaches can effectively distinguish plastic types and other debris categories to support automated ocean monitoring.