Dual-Energy X-Ray Battery Detection in Layered Waste Streams
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Solution Overview
Problem
Current methods for detecting lithium-ion batteries in waste are ineffective due to their integration in multi-layered products, making manual sorting necessary and automatic detection difficult.
Innovation Solution
An object detection system using a dual-energy X-ray imager to generate images that distinguish between materials, combined with a trained model to identify and locate lithium-ion batteries, and an output device for notification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual sorting is used to detect lithium-ion batteries in waste, then detection reliability can be maintained through human judgment, but productivity is severely reduced and labor costs increase
Solution Approach 1:
The patent replaces manual mechanical sorting with an automated optical detection system using cameras and image processing algorithms. The system captures images of waste materials, processes them through trained models to identify lithium-ion batteries, and automatically sorts them, thereby maintaining detection reliability while dramatically improving productivity.
Solution Approach 2:
The system enables self-service detection by equipping the sorting facility with autonomous detection capabilities. The image processing system independently identifies and locates lithium-ion batteries without requiring human operators to manually inspect each item, allowing the system to serve itself in the detection task while freeing human workers for higher-level supervision.
2Productivity
If automatic detection methods are implemented, then productivity increases and labor costs decrease, but detection precision deteriorates due to the complexity of multi-layered waste structures
Solution Approach 1:
The patent segments the detection task into multiple specialized components: image acquisition, pre-processing to enhance contrast and remove noise, feature extraction to identify battery-specific characteristics, and classification using trained models. This segmentation allows each component to be optimized independently, maintaining high detection precision while enabling automated high-speed processing.
Solution Approach 2:
The system performs preliminary image processing operations including contrast enhancement, noise filtering, and edge detection before the main classification step. These preliminary actions prepare the images by emphasizing battery-related features and suppressing background interference, thereby improving detection precision in subsequent automated processing stages.
3Speed
If simple image processing is used, then processing speed increases, but detection accuracy decreases due to the ability to distinguish materials
Solution Approach 1:
The patent transforms images into multiple parameter spaces including grayscale conversion, contrast enhancement, and edge detection maps. By representing the same visual information in different parameter forms, the system enables rapid processing through simple operations on transformed data while preserving the ability to distinguish complex materials through multi-parameter analysis.
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
Enhances detection accuracy and automates the sorting process, reducing the risk of fires caused by undetected lithium-ion batteries in waste.
Implementation Method 1
a technology for detecting a particular object mixed in with waste using an X-ray imager
Data Source
AI summary
An object detection apparatus includes circuitry to acquire a multi-energy X-ray image based on a plurality of types of X-ray data from an X-ray imager, generate a first image in which a target object appears, and detect a position of the target object in the first image using a first trained model that is pretrained to output the position of the target object.