Object Detection Model Blind Spot Correction via Grid Evaluation
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Solution Overview
Problem
Object detection models in electronic devices often perform poorly when detecting targets in specific regions of an image, leading to blind spots due to inadequate training data, which can result in false positives or negatives, and existing evaluation metrics like mAP do not fully capture these performance issues.
Innovation Solution
An automated method for generating evaluation and training image datasets that systematically identify and address blind spots by placing target objects in various locations within a grid, allowing the model to be trained specifically to improve detection accuracy in those regions using transfer learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object detection models are trained with general training data, then overall detection capability is achieved, but detection precision in specific regions deteriorates due to blind spots
Solution Approach 1:
The evaluation dataset is divided into multiple regions (e.g., grid cells) to systematically identify blind spots in different areas. This segmentation allows the model to be evaluated and retrained region-by-region, improving detection precision in specific areas without compromising overall reliability.
Solution Approach 2:
The patent applies local quality by creating region-specific training data for blind spot regions. Instead of uniform training across all regions, the system generates targeted training samples for areas where the model performs poorly, thereby improving detection precision locally while maintaining overall model reliability.
2Measurement precision
If evaluation metrics like mAP are used, then overall model performance is measured, but detection accuracy in specific locations and sizes is not fully captured
Solution Approach 1:
The patent extends the evaluation from traditional mAP metrics to a multi-dimensional evaluation space that includes spatial location and object size dimensions. By dividing the image into grid regions and evaluating detection performance in each region separately, the system captures blind spot information that would be lost in aggregate mAP metrics.
Solution Approach 2:
The system implements feedback by using evaluation results to identify blind spots and then generating targeted training data for those specific regions. This closed-loop feedback mechanism ensures that detection accuracy improvements are continuously targeted at previously missed locations and sizes, preventing information loss about model weaknesses.
3Reliability
If manual evaluation and dataset generation is performed, then detection blind spots can be identified, but time consumption and operational complexity increase
Solution Approach 1:
The system implements self-service by automatically generating evaluation datasets and identifying blind spots without manual intervention. The automated pipeline includes random sampling of images, automatic annotation generation, model evaluation, blind spot identification, and targeted retraining, significantly reducing the time and operational complexity compared to manual processes.
Solution Approach 2:
The patent applies preliminary action by pre-generating large amounts of training data with various annotations (bounding boxes, segmentation masks, key points) before evaluation. This pre-prepared data pool enables rapid, automated evaluation and retraining cycles, reducing the time required for each iteration of blind spot identification and correction.
4Measurement precision
If comprehensive training data covering all possible object locations and sizes is provided, then detection precision across all regions improves, but device complexity and training data requirements increase
Solution Approach 1:
The system applies partial action by focusing training efforts only on blind spot regions rather than uniformly training all regions. By identifying specific areas where detection fails and generating training data only for those regions, the system improves detection precision across all regions while avoiding the excessive complexity of comprehensive uniform training data generation.
Data Source
AI summary
Examples of electronic devices are described herein. In some examples, an electronic device includes a processor to generate an evaluation image dataset to determine precision of a machine learning object detection model. In some examples, the processor is to run the evaluation image dataset on the object detection model to identify a misdetection region in the evaluation image dataset. In some examples, the processor is to generate a training image dataset to adjust the object detection model based on the identified misdetection region.