Image Sentiment Assessment for Hazard Detection
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Solution Overview
Problem
Current methods fail to effectively assess and address environmental and health risks associated with hazardous waste, litter, unhygienic medical facilities, and illicit online pharmacies, which pose significant challenges to human and environmental well-being.
Innovation Solution
A computer-implemented method and system for assessing image sentiment, which involves receiving an image, identifying regions of interest, determining sentiment values based on classification, and displaying these values with geographical locations, utilizing neural networks and image processing techniques to identify and quantify risks such as litter and unhygienic conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual assessment methods are used to evaluate environmental and health risks, then human judgment and expertise can be applied, but the process is time-consuming and lacks real-time capability
Solution Approach 1:
The patent replaces manual human assessment with an automated image processing system that uses computer vision and machine learning algorithms to detect and evaluate environmental hazards, litter, and unhygienic conditions. This substitution of mechanical/automated systems for human manual processes enables real-time risk assessment while maintaining or improving accuracy through consistent algorithmic evaluation.
Solution Approach 2:
The system introduces an intermediary layer of image analysis technology between the hazard detection and risk assessment processes. By capturing images and processing them through trained models, the system creates an automated intermediary assessment mechanism that provides real-time feedback without requiring direct human intervention for each evaluation.
2Measurement precision
If comprehensive image analysis is performed to identify all types of hazards and risks, then assessment accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the complex image analysis task into distinct specialized models: one for environmental hazard detection, another for litter identification, and a third for unhygienic condition assessment. Each model is trained specifically for its designated task, allowing the system to achieve high accuracy across multiple hazard types while managing complexity through modular architecture.
Solution Approach 2:
The system employs a universal image processing framework that handles multiple types of hazards through a common technical approach. The unified system architecture processes different hazard types (environmental, litter, unhygienic conditions) using the same fundamental image analysis pipeline, reducing overall system complexity compared to having separate dedicated systems for each hazard type.
3Speed
If real-time image processing is implemented to provide immediate risk assessment, then response time improves, but computational resource requirements increase
Solution Approach 1:
The patent implements preliminary action by pre-training multiple specialized models offline before deployment. During real-time operation, the system only needs to execute pre-configured detection algorithms rather than performing complex training computations. This preliminary preparation enables fast real-time assessment while minimizing ongoing computational energy requirements.
Data Source
AI summary
A computer-implemented method for assessing an image sentiment. The method can include receiving an image to identify a region of interest in the image; determining an image sentiment value associated with the image based on the region of interest, or a classification of the image; and displaying an indication of the image sentiment value with a spatial (e.g. geographical) location.