A review of computer vision applications in litter and cleanliness monitoring

Keyphrases

• Computer Vision 100%
• Cleanliness 100%
• Computer Vision Applications 100%
• Dataset Curation 100%
• Cost Reduction 50%
• Environmental Conditions 50%
• Comparative Analysis 50%
• Extensive Application 50%
• Cost Efficiency 50%
• Machine Learning Models 50%
• Error Rate 50%
• Study Review 50%
• Random Forest 50%
• Data Accuracy 50%
• Urban Areas 50%
• Efficiency Improvement 50%
• Convolutional Neural Network 50%
• Municipal Solid Waste Management 50%
• Deep Learning Methods 50%
• Machine Learning Algorithms 50%
• Score Value 50%
• Urban Waste Management 50%
• Training Model 50%
• Region-based Convolutional Neural Network (R-CNN) 50%
• Precision Error 50%
• Mean Average Precision 50%
• YOLOv5 50%
• Five Steps 50%
• PRISMA 2020 50%
• Manual Processes 50%
• Street Cleaning 50%
• Dataset Usage 50%
• Litter Detection 50%

Earth and Planetary Sciences

• Computer Vision 100%
• Machine Learning 66%
• Waste Management 33%
• Preprocessing 33%
• Municipal Solid Waste Management 33%
• Comparative Analysis 33%

Agricultural and Biological Sciences

• YOLO 33%