Cascade Object Detection with Threshold Scoring for Real-Time Accuracy
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Solution Overview
Problem
Existing pedestrian detection technologies face challenges in achieving high detection accuracy with efficient computation resources, particularly in real-time applications such as driving assistance and intelligent monitoring systems.
Innovation Solution
An object detection method utilizing a cascade classifier composed of differential channel random decision forest (DCRDF) classifiers, trained with positive and negative sample images, to enhance detection accuracy and speed by leveraging features extracted through algorithms like HOG and LBP, and setting optimal cascade thresholds to balance miss and false detection rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object detection methods are used to ensure high detection accuracy, then detection precision is improved, but computational complexity and processing time increase
Solution Approach 1:
The detection process is divided into multiple stages with a cascade classifier containing multiple levels of classifiers. Each level performs progressively more complex analysis, allowing simple rejection of obvious non-objects early in the process while reserving complex computation for regions that require detailed examination. This segmentation of the detection process into hierarchical stages reduces overall computational complexity while maintaining high detection accuracy for true objects.
Solution Approach 2:
Different regions of the image receive different levels of processing intensity. The cascade classifier applies increasingly sophisticated analysis only to regions that pass previous filtering stages, rather than uniformly processing the entire image. This local differentiation of processing quality allows the system to achieve high detection accuracy for potential objects while minimizing computational waste on background regions.
2Measurement precision
If traditional object detection methods are used to ensure high detection accuracy, then detection precision is improved, but processing speed decreases
Solution Approach 1:
The detection process is segmented into multiple cascaded stages where each stage processes only the regions that passed the previous stage. This segmentation enables early termination for negative cases (background regions) while maintaining thorough analysis for positive cases (potential objects), thereby improving processing speed without sacrificing detection accuracy for true objects.
Solution Approach 2:
The cascade classifier performs partial analysis at early stages and reserves excessive (full) analysis for later stages only when necessary. Simple feature checks are performed on all regions first, and only regions that require more detailed examination proceed to computationally intensive analysis. This partial action approach accelerates processing by avoiding unnecessary full analysis of background regions while ensuring accurate detection of true objects.
3Productivity
If simple detection methods are used to improve processing speed, then computational efficiency is improved, but detection accuracy deteriorates
Solution Approach 1:
The detection system dynamically adjusts the level of analysis applied to different image regions based on their characteristics and the results of previous detection stages. Regions that pass early simple filters receive progressively more dynamic and sophisticated analysis in subsequent cascade stages. This dynamic adaptation allows the system to maintain high processing speed for obvious cases while achieving high detection accuracy for challenging cases that require more complex analysis.
Data Source
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AI summary
Embodiments of the disclosure provide an object detection method and device. The object detection method includes: extracting features of an image; classifying the image by each level of classifiers of a cascade classifier according to the features of the image, and calculating a classification score of the image in each level of the classifiers of the cascade classifier according to a classification result; and calculating, according to the classification score, a cascade score of the image in a corresponding level of the cascade classifier, comparing the cascade score in the corresponding level with a cascade threshold of the corresponding level, and judging the presence of an object in the image according to a comparison result.