Smart Waste Underwater Segmentation using Deep Learning
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Questions and Answers

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?

  • VGG
  • LeNet
  • Mask RCNN (correct)
  • AlexNet

    • The environment and work tools used for implementation include TensorFlow.

    <p>True</p> Signup and view all the answers

    What is the domain and field for the research conducted by BENDIB Mohamed Dhia?

    <p>Domain: Mathematics and Computer Science, Field: Computer Science</p> Signup and view all the answers

    Who supervised the research conducted by BENDIB Mohamed Dhia?

    <p>Dr.BOURAIGAA Salima</p> Signup and view all the answers

    Waste sorting plays a crucial role in efficient waste management.

    <p>True</p> Signup and view all the answers

    What are the two trained models proposed for waste segmentation in the research?

    <p>YOLOv8 and Mask RCNN</p> Signup and view all the answers

    What was the average precision achieved by the YOLOv8 model after 200 training epochs?

    <p>84%</p> Signup and view all the answers

    What dataset was the proposed system tested on?

    <p>Underwater_debris</p> Signup and view all the answers

    What is the term used to describe the process of separating waste to facilitate recycling?

    <p>sorting</p> Signup and view all the answers

    What are some benefits of recycling waste? (Select all that apply)

    <p>Recovery of raw resources</p> Signup and view all the answers

    Manual waste separation incurs low costs.

    <p>False</p> Signup and view all the answers

    Match the following acronyms with their full meanings:

    <p>DNNs = Deep Neural Networks CNN = Convolutional Neural Network DL = Deep Learning GAN = Generative Adversarial Neural Network</p> Signup and view all the answers

    Most nations have defined waste, which is typically associated with the idea of ____.

    <p>disposal</p> Signup and view all the answers

    In what year was the first landfill established in Knossos, Crete, Greece?

    <p>3000 B.C.</p> Signup and view all the answers

    In Athens, Greece, waste disposal legislation mandated that waste be disposed of within the city limits.

    <p>False</p> Signup and view all the answers

    The first anti-littering rule was passed in New Amsterdam and forbids throwing or leaving waste on the ____________.

    <p>streets</p> Signup and view all the answers

    Who launched the first street cleaning business in 1757?

    <p>Ben Franklin</p> Signup and view all the answers

    In what year was America's first incinerator constructed at New York City's Governors Island?

    <p>1885</p> Signup and view all the answers

    What does the FC layer in a neural network usually employ?

    <p>SoftMax function</p> Signup and view all the answers

    Which CNN algorithm was developed by Patrick Haffner, Yoshua Bengio, Leon Bottou, and Yann LeCun to identify handwritten characters?

    <p>LeNet</p> Signup and view all the answers

    AlexNet emerged victorious in the 2012 ImageNet Large Scale Visual Recognition Challenge.

    <p>True</p> Signup and view all the answers

    What architecture is known for using residual blocks to handle fading gradients in very deep networks? __________

    <p>ResNet</p> Signup and view all the answers

    Match the following DL model with its domain:

    <p>RCNN = Object detection Faster RCNN = Object detection U-Net = Segmentation</p> Signup and view all the answers

    What is the main factor contributing to the 2 billion metric tons of solid waste produced each year?

    <p>Population growth</p> Signup and view all the answers

    What is the goal of smart waste management?

    <p>To use intelligent systems to address waste management issues</p> Signup and view all the answers

    Smart waste management may lead to lower operational expenses and cash flow.

    <p>True</p> Signup and view all the answers

    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.

    <p>data-driven</p> Signup and view all the answers

    Match the benefits of smart waste management with their descriptions:

    <p>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</p> Signup and view all the answers

    What are the two main types of deep learning techniques mentioned for sorting underwater waste?

    <p>Segmentation techniques and object detection techniques</p> Signup and view all the answers

    How many waste categories are the suggested segmentation techniques able to classify the wastes into?

    <p>8</p> Signup and view all the answers

    The proposed models' performance was assessed using an underwater-debris dataset from Roboflow.

    <p>True</p> Signup and view all the answers

    What were the two suggested methods for underwater waste segmentation in the work?

    <p>Mask RCNN and YOLOv8</p> Signup and view all the answers

    What discipline aims to break down intelligence into elementary functions to create machines mimicking human cognitive abilities?

    <p>Artificial Intelligence</p> Signup and view all the answers

    Artificial Intelligence is often mentioned together with Machine Learning and Deep Learning.

    <p>True</p> Signup and view all the answers

    What type of learning trains computers on labeled datasets to predict outputs?

    <p>Supervised machine learning</p> Signup and view all the answers

    _______ uses a down sampling or pooling layer to lower the input's dimensionality in a CNN.

    <p>Pooling</p> Signup and view all the answers

    Match the following machine learning types with their descriptions:

    <p>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</p> Signup and view all the answers

    What kind of tasks can computer vision enable a computer to do?

    <p>sense and recognize objects in its environment</p> Signup and view all the answers

    Which algorithm had the best performance in detecting marine litter according to Fulton et al.?

    <p>Faster RCNN</p> Signup and view all the answers

    Computer vision is better than human vision in all scenarios.

    <p>False</p> Signup and view all the answers

    The process by which the eyes recognize and convert light into images is known as ________ vision.

    <p>human</p> Signup and view all the answers

    Match the following projects with their models:

    <p>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</p> Signup and view all the answers

    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

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    Description

    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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