Adaptive Threshold Object Detection via Histogram Regression
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Solution Overview
Problem
Existing object detection systems face challenges due to the use of a single global threshold value for all images, which is inadequate in handling the variability of object appearances, environments, and image quality, leading to suboptimal detection results.
Innovation Solution
The development of adaptive thresholding systems and methods that train models to predict optimal threshold values for each image by computing normalized histograms of detector response values, minimizing false positives while maximizing true positives, using regression models like neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single global threshold value is used for all images, then the detection system is simple and fast, but the detection accuracy deteriorates due to inability to handle variability in object appearances, environments, and image quality
Solution Approach 1:
The patent implements dynamic thresholding by training a regression model (e.g., neural network) that predicts optimal threshold values adaptively for each input image based on its histogram characteristics. This replaces the static global threshold with a dynamic, image-specific threshold that adjusts to varying detection conditions, thereby improving detection accuracy without requiring manual intervention for each image
Solution Approach 2:
The patent changes the threshold parameter from a fixed global value to a variable that is predicted by a machine learning model. The model learns optimal threshold values during training by minimizing false positives while preserving true positives, and applies these learned parameters during detection. This parameter transformation enables the system to adapt to different image characteristics and detection scenarios
2Measurement precision
If a high threshold value is used to reduce false positives, then precision improves, but recall deteriorates as true positives are excluded
Solution Approach 1:
The patent employs feedback through a trained regression model that learns from training data the optimal balance between precision and recall. The model receives histogram information as input and outputs a threshold value that has been optimized during training to achieve both high precision and high recall. This feedback mechanism allows the system to automatically adjust thresholds based on image characteristics rather than using fixed high thresholds that sacrifice recall
3Reliability
If a low threshold value is used to preserve true positives, then recall improves, but precision deteriorates due to increased false positives
Solution Approach 1:
The trained regression model provides feedback-based threshold selection that simultaneously optimizes for both recall and precision. By learning from training data that includes both true positives and false positives, the model identifies threshold values that maintain high recall while filtering out false positives through the learned decision boundary encoded in the model weights
4Measurement precision
If adaptive thresholding with trained models is implemented, then detection accuracy improves by providing image-specific thresholds, but system complexity and training requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training the regression model offline using labeled training data. This training phase computes optimal threshold values for many examples and encodes the learned patterns into the model weights. During actual detection, the pre-trained model quickly predicts thresholds without requiring real-time training, thus improving detection accuracy while keeping runtime complexity manageable through the advance preparation
Data Source
AI summary
Systems and methods for developing and using adaptive threshold values for different input images for object detection are disclosed. In embodiments, detector response histogram-based systems and methods train models for predicting optimal threshold values for different images. In embodiments, when training the model, an optimal threshold value for an image is defined as the value that maximizes the reduction of false positive image patches while preserving as many true positive image patches as possible. Once trained, the model may be used to set different threshold values for different images by inputting a detector response histogram for the image patches of an image into the model to determine a threshold value for detection.