Optical Sensor Waste Detection Using Iterative Parameter Analysis
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Solution Overview
Problem
Current waste detection systems in urban areas are inefficient due to lack of automated interpretation, high energy consumption, and limited privacy protection, often relying on simple optical sensors that require manual data retrieval and lack continuous monitoring capabilities.
Innovation Solution
A method and system using an optical sensor to generate and compare models of waste items detected with different sets of parameters, incrementing a counter when the same waste item is consistently identified, triggering an alert signal when the counter exceeds a threshold, allowing for automatic and continuous monitoring while respecting privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video recording or photographic acquisition technologies are used to detect waste, then waste presence can be recorded, but manual data retrieval and analysis is required which is time-consuming and money-consuming
Solution Approach 1:
The system enables self-service by automatically analyzing captured images through AI/ML algorithms to identify waste items, eliminating the need for manual data retrieval and analysis. The automated processing generates alerts directly when waste is detected, making the system self-sufficient in both detection and interpretation phases.
Solution Approach 2:
The patent replaces manual mechanical processes (physical retrieval and human analysis of images) with automated electronic systems including AI/ML algorithms and computer vision technologies. This substitution transforms the workflow from manual intervention to automated digital processing, significantly reducing time and resource requirements.
2Loss of information
If remote transmission of data is carried out for the entire duration of detected events, then complete monitoring information is available, but high energy consumption occurs that cannot be supported without direct electrical connection
Solution Approach 1:
Instead of continuous transmission, the system employs periodic action by transmitting data only at specific intervals or when significant events occur (waste detection). The automated analysis filters out unnecessary transmissions, sending information only when waste items are identified, thus reducing energy consumption while maintaining monitoring effectiveness.
Solution Approach 2:
The system extracts only the essential information needed for waste detection and transmission, rather than continuously transmitting all captured data. By using AI/ML algorithms to analyze images locally and transmit only relevant waste-related information, the system minimizes data transmission volume and associated energy consumption.
3Reliability
If all changes in monitored scene trigger recordings, then no waste detection is missed, but privacy is compromised as all events are indiscriminately reported
Solution Approach 1:
The system implements feedback through automated AI/ML analysis that continuously evaluates captured images to determine whether waste is present. This feedback mechanism allows the system to distinguish between significant events (waste detection) and normal activities, triggering alerts only when waste items are identified rather than for all scene changes, thus maintaining reliability while protecting privacy.
Solution Approach 2:
The patent applies parameter changes by using AI/ML algorithms to analyze multiple parameters of captured images (object characteristics, patterns, contextual information) rather than simply detecting motion. This sophisticated parameter analysis enables the system to identify waste items accurately while filtering out privacy-infringing data from legitimate human activities.
4Device complexity
If simple optical sensors are used, then device complexity is reduced, but automated interpretation capability is lost making proactive alarm generation impossible
Solution Approach 1:
The system achieves multi-functionality by integrating simple optical sensors with AI/ML algorithms and automated analysis capabilities into a single unified platform. The optical sensors capture images, while the integrated software performs automated waste identification and alert generation, allowing the system to maintain relative simplicity while gaining advanced automation capabilities through software integration.
Data Source
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Figure 2A~2B
AI summary
A method for identifying waste is executed by monitoring a detection area by means of an optical sensor. Operationally, the presence of waste is checked several times by controlling the optical sensor according to a plurality of sets of distinct parameters and if the examination provides the same result each time (i.e. the same waste item is detected in several successive iterations) a corresponding counter is increased. When the counter exceeds a set threshold value, an alert signal representative of the presence of a waste item to be removed in the detection area is generated.