Optical Sensor Auditing Waste Container Fill Volume
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Solution Overview
Problem
Waste services providers face inefficiencies and inaccuracies in visually auditing the fill status of customer waste containers, leading to potential accidents and billing inaccuracies due to overloaded containers.
Innovation Solution
A system equipped with an optical sensor on waste collection vehicles captures image data to determine the fill status of containers, comparing it to a predetermined threshold using machine learning, and generates action proposals for communication, billing adjustments, or container recovery instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual auditing by driver or employee is used, then no additional equipment is needed, but accuracy and efficiency of fill status detection deteriorates
Solution Approach 1:
The patent replaces the manual visual inspection mechanism with an automated optical sensing system. The optical sensor captures images of the waste container, and machine learning algorithms automatically analyze the fill status, substituting human visual judgment with automated image processing to improve measurement precision while accepting increased device complexity.
Solution Approach 2:
The patent introduces an intermediary optical sensing system between the waste container and the auditing process. The optical sensor acts as a mediator that captures visual data, which is then processed by machine learning algorithms to determine fill status, creating an intermediate layer that enhances detection accuracy without requiring direct human observation.
2Productivity
If visual auditing is used, then equipment cost is low, but productivity and efficiency of waste service activity deteriorates
Solution Approach 1:
The auditing system performs self-service by automatically capturing images and analyzing fill status without requiring human intervention. The machine learning model autonomously processes the optical sensor data to determine whether the container is overloaded, enabling the system to service itself and improve productivity while accepting the complexity of the automated components.
Solution Approach 2:
The patent replaces the manual auditing process with an automated optical sensing and machine learning system. This substitution eliminates the need for employees to manually inspect each container, significantly improving productivity and efficiency of waste service activities despite the increased device complexity.
3Reliability
If visual auditing is used, then implementation is simple, but reliability of overload identification deteriorates
Solution Approach 1:
The system implements feedback by using machine learning algorithms that continuously analyze optical sensor data to determine fill status. The system provides reliable identification of overloaded containers by comparing image data against learned patterns of overload conditions, accepting the complexity of the machine learning infrastructure to ensure high reliability.
Solution Approach 2:
The patent replaces unreliable human visual judgment with a reliable automated optical sensing and machine learning system. The machine learning model provides consistent and reliable overload identification by objectively analyzing image data, eliminating human error and variability while accepting the increased device complexity.
4Measurement precision
If manual visual inspection is used, then response time is fast, but measurement precision of fill status deteriorates
Solution Approach 1:
The optical sensor system enables continuous auditing by automatically capturing and analyzing images of waste containers in real-time during waste service activities. The machine learning model continuously processes the visual data to determine fill status, maintaining continuous useful action without interruption or manual intervention, thereby improving measurement precision while minimizing time loss through automated real-time processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This system improves accuracy in identifying overloaded containers, enhancing safety and billing precision while reducing equipment damage and employee risk, and providing proactive customer service and education.
Implementation Method 1
an optical sensor disposed on a waste collection vehicle and configured to capture image data of the customer waste container that is indicative of the fill status of the container
Data Source
AI summary
Systems and methods are provided for using video/still images captured by continuously recording optical sensors mounted on waste collection vehicles used in in the waste collection, disposal and recycling industry for operational and customer service related purposes. A system is provided for auditing the fill status of a customer waste container by a waste services provider during performance of a waste service activity.