Image Drift Detection Using Segment Statistics at the IoT Edge
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Solution Overview
Problem
Data drift in unstructured image data, particularly in industrial settings, poses challenges for machine learning models due to temporary occlusions, environmental changes, and camera shifts, affecting accuracy and integrity, with existing methods struggling to detect drift without prior access to training data.
Innovation Solution
A method and system that partitions images into segments, computes drift within each segment, and combines these to determine overall drift, allowing detection and corrective action at the IoT edge without relying on training data, using an IoT edge gateway to minimize latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If drift detection is performed on unstructured image data in industrial settings, then model accuracy and reliability can be maintained, but detection complexity increases due to the unstructured nature of image data compared to structured data
Solution Approach 1:
The patent divides an image into multiple segments and performs drift detection on each segment independently. This segmentation approach reduces the complexity of detecting drift in unstructured image data by breaking down the large unstructured dataset into smaller, more manageable segments, while still maintaining overall model reliability through comprehensive segment-level monitoring
Solution Approach 2:
The patent introduces segment-level statistics (mean, standard deviation, histogram) as intermediary representations between the raw unstructured image data and the drift detection algorithm. These intermediary statistical features transform complex unstructured image data into simplified numerical representations that are easier to analyze for drift detection while preserving essential visual characteristics
2Measurement precision
If segment-level drift detection is implemented, then detection precision can be improved by identifying specific drifted regions, but computational overhead increases due to processing multiple segments
Solution Approach 1:
By segmenting images into smaller regions, the patent achieves more precise drift detection at the segment level, allowing identification of specific drifted regions. The computational overhead is managed by using efficient statistical computations (mean, standard deviation, histogram) on each segment rather than complex processing, balancing precision with computational efficiency
Solution Approach 2:
The patent computes drift metrics for all segments but may focus corrective actions only on segments showing significant drift. This partial action approach maintains high detection precision by evaluating all segments while reducing overall computational overhead by not uniformly processing every segment with full analysis
3Adaptability or versatility
If drift detection is performed without access to training data, then deployment flexibility is improved, but detection accuracy may worsen due to lack of comparison baseline
Solution Approach 1:
The patent pre-computes and stores segment-level statistics (mean, standard deviation, histogram) during the model training phase and stores them in a drift database. This preliminary action creates a baseline for comparison that enables accurate drift detection during deployment without requiring access to the original training data, thus maintaining both deployment flexibility and detection accuracy
Solution Approach 2:
The patent creates a copy of essential training data characteristics in the form of segment-level statistical features stored in the drift database. These copied statistical representations serve as a surrogate for the actual training data, enabling accurate drift detection comparisons during deployment while maintaining deployment flexibility by not requiring the original training datasets
4Loss of time
If quick drift detection is implemented for timely corrective actions, then response time is reduced, but detection thoroughness may be compromised to achieve faster results
Solution Approach 1:
The patent segments images and processes them in parallel, which reduces overall response time compared to processing entire images sequentially. Detection thoroughness is maintained by ensuring all segments are evaluated and by using comprehensive statistical metrics (mean, standard deviation, histogram) for each segment, achieving both speed and thoroughness through parallel segment processing
Data Source
AI summary
A method for computing and detecting image data drift. The method may include retrieving first segment information of a plurality of segments from a drift database; receiving a number of images from a sensor; partitioning each of the received images into segments of a predetermined number; generating second segment information; computing drift in values between the first segment information and the second segment information; and detecting drift based on the computed drift in values by combining the computed drift in segments to generate overall drift, and comparing the overall drift against a drift threshold.