Crane System Quality Assessment Using Neural Network Image Analysis
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Solution Overview
Problem
Current quality inspection methods in industrial environments, particularly in container terminals and crane operations, rely heavily on human intervention, leading to inefficiencies, inconsistencies, and high costs due to manual labor and lengthy machine learning training times.
Innovation Solution
A crane system equipped with high-definition LIDAR cameras and a computing unit featuring a trained artificial neural network, which employs supervised machine learning and image processing techniques to assess the quality of objects in real-time, reducing the need for human intervention and minimizing training time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human inspectors perform quality sorting, then quality assessment can be conducted, but it requires heavy manual labor costs and is time consuming
Solution Approach 1:
The patent replaces the mechanical human inspection process with an automated computer vision system that uses image processing algorithms and machine learning models to detect defects, classify objects, and assess quality automatically, eliminating manual labor while maintaining or improving inspection accuracy and speed
Solution Approach 2:
The system enables self-service quality inspection where the computer vision system autonomously performs defect detection, object classification, and quality assessment without human intervention, with the AI model continuously learning and improving from processed data
2Extent of automation
If machine learning techniques are used for defect identification, then automation is improved, but training time becomes quite long
Solution Approach 1:
The patent implements preliminary action by pre-training the machine learning model with a curated dataset of industrial objects and defects before deployment, and by implementing online learning capabilities that allow the model to continue training on new data without stopping production, thus reducing the impact of training time on operational efficiency
Solution Approach 2:
The system optimizes training time by adjusting hyperparameters, using transfer learning from pre-trained models, and implementing efficient data augmentation techniques that reduce the amount of training data needed while maintaining model performance
3Measurement precision
If unsupervised learning approach is used with image segmentation, then defect identification is enabled, but processing power and memory consumption increase
Solution Approach 1:
The patent applies segmentation by dividing the image processing task into distinct stages: pre-processing, feature extraction, defect detection, and classification. This segmented approach allows each stage to be optimized independently and enables parallel processing to reduce overall computational burden
Solution Approach 2:
The system extracts only the essential features and defect-related information from images using targeted feature extraction algorithms, discarding redundant data early in the processing pipeline to reduce memory usage and computational requirements for subsequent analysis stages
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
The system enables efficient, time-saving, and cost-effective quality assessment of objects, reducing manual labor costs and improving the accuracy and consistency of quality inspections, while also minimizing CO2 emissions from training processes.
Implementation Method 1
The cameras comprise, for example, high definition light detection and ranging (LIDAR) cameras capable of capturing high definition real time images of the object
Data Source
AI summary
A method for managing a crane system capable of handling an object using an artificial neural network, a crane system, a method for training the artificial neural network, and a computer program product code are provided. The method includes generating an image data stream based on multiple images of the object captured by cameras of the crane system, analyzing the image data stream by employing a computing unit of the crane system using the artificial neural network trained for identifying markers from the image data stream, determining, by the computing unit, object properties associated with the object based on the analysis of the image data stream, wherein the object properties comprise at least a quality of the object, and automatically operating the crane system for handling the object based on the object properties.