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February 21, 2026Open Access

AI Systems for Visual Waste Recognition and Disposal Coordination in Urban Areas:A Survey of Techniques, Datasets, and Gaps

Key Points

The review aims to summarize advancements in AI techniques for visual waste recognition and disposal coordination, highlighting key challenges and research gaps.
Reviewed multiple studies on AI-driven waste recognition and disposal systems
Evaluated techniques such as CNN classifiers, YOLO, and Mask R-CNN
Analyzed challenges in dataset diversity and performance dynamics
Identified critical needs for real-time integration and spatial precision.
Current AI systems show improved accuracy in controlled environments but struggle in dynamic settings
Limited dataset diversity hinders generalization of models
Integration of classification and segmentation is necessary for better spatial accuracy and content understanding
Identified key gaps in research for scalable AI waste management solutions.

Abstract

The aim of this review is to summarize the development in the field of AI driven visual waste recognition and disposal coordination. Achieved by sampling a multitude of studies reflecting the transition from traditional techniques to the advancement such as hybrid deep learning integrating object detection, segmentation and real time analysis. The initial attempts used CNN classifiers having limitations in generalization and deployment. Latter improvements utilizing models like YOLO, Mask R-CNN and edge based model gives better accuracy in an controlled environment However, there are persistent challenges like limited dataset diversity, unstable performance in dynamic conditions and lack of real-time integration. It was observed that simultaneously implementing both classification and segmentation are critical to enable spatial precision and content understanding is a critical observation. This review identifies key research gaps and presents future directions for scalable, context-aware AI waste systems.

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