Pollution Level Estimation Using Image-Based Garbage Detection
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Solution Overview
Problem
Existing methods for determining pollution levels at coastal areas rely heavily on subjective investigator evaluations, leading to inaccurate assessments.
Innovation Solution
A pollution level estimation system that acquires images of the area, detects garbage and non-garbage portions using machine learning, and calculates pollution levels based on the detected areas, optionally weighting by distance or counting garbage pieces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pollution level is determined based on investigator's visual observation and subjective evaluation, then the investigation process is simple and quick, but the measurement precision and reliability are poor
Solution Approach 1:
The patent replaces the mechanical system of manual visual observation with an automated image processing system using machine learning. The detection unit automatically identifies garbage portions in images captured by a camera, eliminating the need for human investigators to visually assess pollution levels. This substitution significantly improves measurement precision while the automated nature of the system keeps operational complexity manageable.
Solution Approach 2:
The patent creates a digital copy of the physical environment by capturing images of the seashore and processing them through machine learning algorithms. Instead of directly observing the physical scene, the system analyzes digital representations (images) that can be processed objectively. This copying approach allows for precise, repeatable measurements without the subjectivity inherent in human visual assessment.
2Reliability
If automated image processing with machine learning is used to detect garbage portions, then measurement precision and objectivity are improved, but device complexity and processing requirements increase
Solution Approach 1:
The machine learning model performs self-service by automatically learning to identify garbage portions from training data. Once trained, the detection unit can autonomously process images and identify garbage without requiring manual intervention or complex configuration. This self-service capability improves reliability by ensuring consistent, objective detection while keeping the system relatively simple to operate.
Solution Approach 2:
The system performs preliminary action by pre-training the machine learning model with labeled data before actual pollution level estimation. This preliminary training phase enables the model to automatically recognize garbage patterns, so that during actual operation, the system can reliably and objectively detect garbage portions without requiring complex real-time processing or human input.
Data Source
AI summary
A pollution level estimation system is a system that estimates a pollution level at a location to be estimated, the system including an image acquisition unit that acquires an image of the location to be estimated, a detection unit that detects a garbage portion showing garbage and a non-garbage portion not showing garbage at the location to be estimated in the acquired image, and a pollution level estimation unit that calculates areas of the detected garbage portion and non-garbage portion and estimates a pollution level at the location to be estimated on the basis of the calculated area.