Autonomous Visual Inspection With ML Severity Assessment
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Solution Overview
Problem
Current object inspection methods, particularly in industries like energy, are laborious, hazardous, and time-consuming due to the need for human intervention, especially in hard-to-reach or remote locations. Additionally, existing machine learning (ML) models face challenges such as expensive and time-consuming annotation processes, limited multi-object detection capabilities, difficulties in measuring severity, and a lack of autonomous pipelines for object detection.
Innovation Solution
An autonomous system and method for inspecting objects using machine learning, which includes automated annotation procedures, transfer learning for multi-object detection, automatic severity level calculation through clustering analysis, failure mode identification using Bayesian networks, and autonomous drone flight management with sensor fusion for improved precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human experts conduct visual inspections of objects in remote or hard-to-reach locations, then inspection accuracy and reliability are improved, but labor costs, time consumption, and safety hazards increase significantly
Solution Approach 1:
The system enables autonomous inspection where the ML model automatically detects objects and features without human intervention. The pipeline performs self-service by autonomously capturing images, processing them through the ML model, and generating inspection reports, eliminating the need for human experts to physically visit remote locations while maintaining inspection reliability
Solution Approach 2:
The patent replaces the mechanical system of human visual inspection with an automated ML-based computer vision system. The ML model substitutes human experts' analytical capabilities, automatically identifying objects and features in images captured by drones or cameras, thereby reducing time consumption while maintaining detection accuracy
2Productivity
If machine learning models are used for automated object detection, then inspection speed and consistency are improved, but annotation costs and model training time increase
Solution Approach 1:
The system performs preliminary actions by pre-training the ML model on annotated datasets before deployment. The annotation process is completed in advance during model development, allowing the deployed system to operate at high speed without real-time annotation requirements. Transfer learning enables the model to adapt to new object types with minimal additional annotation
Solution Approach 2:
The patent uses transfer learning where a pre-trained ML model (copy of knowledge from source domain) is adapted to new inspection tasks. The model copies feature extraction capabilities from training data and applies them to detect different object types, reducing the need for extensive re-annotation while maintaining detection accuracy
3Adaptability or versatility
If traditional visual inspection methods are used, then flexibility in handling diverse object types is maintained, but inspection consistency and objectivity decrease due to human subjectivity
Solution Approach 1:
The ML model is designed with universal functionality to detect multiple types of objects and features across different industries and applications. The same core model architecture can be configured to inspect power lines, buildings, infrastructure, or other objects by adjusting training data and detection parameters, providing both consistency and adaptability
Solution Approach 2:
The system achieves adaptability through parameter changes in the ML model configuration. By adjusting detection thresholds, confidence levels, and model parameters, the system can be optimized for different object types and inspection requirements while maintaining consistent and objective detection results across all applications
4Measurement precision
If manual annotation processes are used to train ML models, then model accuracy can be improved, but the annotation process becomes expensive and time-consuming
Solution Approach 1:
The system performs annotation work in advance during the model training phase. Annotated datasets are prepared beforehand to train the ML model, allowing the deployed inspection system to operate without real-time human annotation. This preliminary annotation action enables fast, automated detection while the annotation effort is concentrated during model development
Solution Approach 2:
The patent uses transfer learning where annotation knowledge is copied from source domains to target domains. The ML model learns from pre-annotated datasets in one domain and applies this knowledge to detect objects in different domains with minimal additional annotation, reducing the time and cost of annotation while maintaining detection accuracy
Data Source
AI summary
Example implementations described herein involve systems and methods for autonomous visual inspection, which may include receiving images autonomously captured via at least one image capturing device; identifying at least one object from the images autonomously based on a set of inference data from a machine learning (ML) module; identifying at least one feature of the at least one object autonomously based on the set of inference data; and initiating an alert autonomously if the at least one feature meets a defined condition or a threshold. In some aspects, the example implementations may further include annotating the at least one object or the at least one feature identified on the images via the ML module; reviewing and reannotating one or more images from the images based on a set of inferenced images; and retraining the ML module based on the one or more images.