44 Questions
What is the primary focus of Chapter 3: Underwater Waste Segmentation design, implementation, and experimentation?
Underwater Waste Segmentation
What is the major aim of the 'Smart Waste Underwater Segmentation: Deep Learning Based Approach' study?
Underwater Waste Segmentation using Deep Learning
Which DL model is proposed for segmentation in the study?
Mask RCNN
The environment and work tools used for implementation include TensorFlow.
True
What is the domain and field for the research conducted by BENDIB Mohamed Dhia?
Domain: Mathematics and Computer Science, Field: Computer Science
Who supervised the research conducted by BENDIB Mohamed Dhia?
Dr.BOURAIGAA Salima
Waste sorting plays a crucial role in efficient waste management.
True
What are the two trained models proposed for waste segmentation in the research?
YOLOv8 and Mask RCNN
What was the average precision achieved by the YOLOv8 model after 200 training epochs?
84%
What dataset was the proposed system tested on?
Underwater_debris
What is the term used to describe the process of separating waste to facilitate recycling?
sorting
What are some benefits of recycling waste? (Select all that apply)
Recovery of raw resources
Manual waste separation incurs low costs.
False
Match the following acronyms with their full meanings:
DNNs = Deep Neural Networks CNN = Convolutional Neural Network DL = Deep Learning GAN = Generative Adversarial Neural Network
Most nations have defined waste, which is typically associated with the idea of ____.
disposal
In what year was the first landfill established in Knossos, Crete, Greece?
3000 B.C.
In Athens, Greece, waste disposal legislation mandated that waste be disposed of within the city limits.
False
The first anti-littering rule was passed in New Amsterdam and forbids throwing or leaving waste on the ____________.
streets
Who launched the first street cleaning business in 1757?
Ben Franklin
In what year was America's first incinerator constructed at New York City's Governors Island?
1885
What does the FC layer in a neural network usually employ?
SoftMax function
Which CNN algorithm was developed by Patrick Haffner, Yoshua Bengio, Leon Bottou, and Yann LeCun to identify handwritten characters?
LeNet
AlexNet emerged victorious in the 2012 ImageNet Large Scale Visual Recognition Challenge.
True
What architecture is known for using residual blocks to handle fading gradients in very deep networks? __________
ResNet
Match the following DL model with its domain:
RCNN = Object detection Faster RCNN = Object detection U-Net = Segmentation
What is the main factor contributing to the 2 billion metric tons of solid waste produced each year?
Population growth
What is the goal of smart waste management?
To use intelligent systems to address waste management issues
Smart waste management may lead to lower operational expenses and cash flow.
True
What type of approach is identified as the only way to succeed in today's technological environment for waste management? Adopting an innovative and __________ approach.
data-driven
Match the benefits of smart waste management with their descriptions:
Optimized Resources = Enhanced accuracy in resource allocation and waste management Reduced expenses = Lower operational costs through data utilization Cleaner Streets = Rapid response to overflowing bins and cleaner areas Better Working Environments = Improved working conditions and productivity for waste collectors Reduced Carbon Emissions = Guided waste management decisions to enhance productivity
What are the two main types of deep learning techniques mentioned for sorting underwater waste?
Segmentation techniques and object detection techniques
How many waste categories are the suggested segmentation techniques able to classify the wastes into?
8
The proposed models' performance was assessed using an underwater-debris dataset from Roboflow.
True
What were the two suggested methods for underwater waste segmentation in the work?
Mask RCNN and YOLOv8
What discipline aims to break down intelligence into elementary functions to create machines mimicking human cognitive abilities?
Artificial Intelligence
Artificial Intelligence is often mentioned together with Machine Learning and Deep Learning.
True
What type of learning trains computers on labeled datasets to predict outputs?
Supervised machine learning
_______ uses a down sampling or pooling layer to lower the input's dimensionality in a CNN.
Pooling
Match the following machine learning types with their descriptions:
Supervised machine learning = Training machines with labeled datasets to predict outputs Unsupervised machine learning = Computer learns on its own from an unlabeled dataset Semi-supervised learning = Combining datasets with and without labels for training Reinforcement learning = Feedback-driven learning seeking to maximize rewards
What kind of tasks can computer vision enable a computer to do?
sense and recognize objects in its environment
Which algorithm had the best performance in detecting marine litter according to Fulton et al.?
Faster RCNN
Computer vision is better than human vision in all scenarios.
False
The process by which the eyes recognize and convert light into images is known as ________ vision.
human
Match the following projects with their models:
Robotic Detection of Marine Litter Using Deep Visual Detection Models = Faster RCNN Underwater and airborne monitoring of marine ecosystems and debris = YOLOv3 Aqua Vision: Automating the detection of waste in water bodies using deep transfer learning = RetinaNet
Study Notes
Introduction
- The importance of waste sorting for efficient waste management
- Automation is a significant factor for waste companies
- Underwater waste cleaning robots use robotics, AI, and autonomous driving to locate and recognize underwater waste
Waste Management
- Waste sorting is a crucial process for efficient waste management
- Waste management involves waste reduction, reuse, recycling, and waste disposal
- Smart waste management involves the use of technology to optimize waste collection and disposal
Underwater Waste Segmentation
- Underwater waste segmentation is a critical process for waste management
- The proposed system uses deep learning-based approach for underwater waste segmentation
- The system is tested on the Underwater_debris dataset
- The system achieves 84% average precision with YOLOv8 model and exceeds 83.3% with Mask RCNN model
Deep Learning Models
- YOLOv8 and Mask RCNN are used for underwater waste segmentation
- YOLOv8 achieves 84% average precision after 200 training epochs
- Mask RCNN exceeds 83.3% average precision after 100 training epochs
Chapter 1: Underwater Waste Management
- Waste definition, history, and management
- Waste hierarchy, waste reduction, and sorting
- Smart waste management and its benefits
Chapter 2: State of the Art (SOTA)
- Artificial intelligence and machine learning
- Deep learning and its applications
- Convolutional Neural Networks (CNNs) and their architectures
- Object detection, classification, and segmentation using deep learning
Chapter 3: Underwater Waste Segmentation, Design, Implementation, and Experimentation
- Overall architecture and data presentation
- Data preprocessing, augmentation, and splitting
- DL models proposed for segmentation (Mask RCNN and YOLOv8)
- Evaluation metrics and implementation details### Introduction to Underwater Waste Management
- Globally, annual solid waste is expected to exceed 2.2 billion tons by 2025, resulting in waste management costs of $375.5 billion.
- Poor waste management can devastate the economy, public health, and the environment.
- The Environmental Protection Agency (EPA) has identified recycling of municipal solid waste (MSW) as the second most environmentally friendly method for managing urban waste.
Waste Definition and History
- Waste is defined as objects or substances that are disposed of, intended to be disposed of, or required to be disposed of by national legislation.
- The concept of waste management has evolved over time, from ancient civilizations to modern-day practices.
- Notable events in the history of waste management include:
- 3,000 B.C.: The first landfill was established in Knossos, Crete, Greece.
- 2,000 B.C.: Recycling and composting techniques were developed in China.
- 500 B.C.: Athens, Greece, passed legislation mandating that waste be disposed of at least a mile outside of the city.
- 1757: Ben Franklin launched the first street cleaning business and encouraged people to dispose of their rubbish by excavating pits in the ground.
Waste Management
- The goal of waste management is to reduce the negative environmental, health, and aesthetic impacts of waste.
- Waste management strategies focus on managing municipal solid waste, which is the main type of waste generated by households, companies, and enterprises.
- Integrated import/export techno-economic systems, efficient disposal locations, circular economy management, and environmentally friendly product design are examples of waste management techniques.
Underwater Waste Segmentation using Deep Learning
- The project aims to segment waste into several categories: mask, metal, glass bottles, plastic bottles, plastic bags, electronics, tires, and other waste.
- The approach uses deep learning methods, specifically Mask RCNN and YOLOv8, to segment, recognize, and classify waste in underwater environments.
- The models achieved a mean average precision (maP) of 83.3% for Mask RCNN and 84% for YOLOv8.Here are the study notes:
Waste Management
- "7R" approach: Refuse, Reduce, Reuse, Repair, Repurpose, Recycle, and Recover for efficient waste management
- Waste hierarchy: Product reduction, reuse, recycling, and prevention, with the last phase being disposal (landfilling or burning without energy recovery)
Waste Life Cycle
- Begins with design, proceeds to manufacturing, distribution, and primary use, and ends with waste hierarchy stages (reduce, reuse, recycle)
Resource Efficiency
- Minimizing environmental impact of creating and using products, from raw material extraction to disposal
Waste Reduction and Sorting
- Reduce: Focus on needs, fewer purchases, and reduced waste management requirements
- Reuse: Using items for different purposes, such as containers for home or school projects
- Recycle: Boosts economy, creates jobs, and cuts expenses; 10,000 tons of debris generates 36 jobs
Waste Management by Region
- Global waste production projected to increase by 70% by 2050
- Population growth is a main contributor to increased waste production
Smart Waste Management
- Uses intelligent systems to address solid waste management issues
- Provides data on waste trends and behavior, enabling sustainable decisions
Underwater Waste Management
- Waste that ends up in seas, oceans, or other large bodies of water
- Sources include boats, offshore structures, land-based waste, and storms
- Difficult to clean due to large amounts and difficulty in reaching underwater waste
Underwater Waste Risks
- Threatens human health, ecosystems, and marine life
- Plastic waste, chemical, and radioactive waste contaminate the ocean
Underwater Waste Reduction and Sorting
- Essential for marine conservation efforts
- Involves gathering, classifying, and processing waste items found underwater
Underwater Waste Valorization
- Adds value to waste materials found underwater through recycling or processing
Conclusion
- Waste management involves reducing, reusing, recycling, and valorizing waste
Artificial Intelligence
- Simulates human intelligence and problem-solving capabilities
- Includes machine learning, deep learning, and computer vision
This quiz covers the concept of smart waste underwater segmentation using a deep learning based approach. It assesses the understanding of deep learning techniques in computer vision and image processing. Test your knowledge of information systems and computer science!
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