Target Object Detection System Using Adaptive Weak Classifier Updates
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Solution Overview
Problem
Advanced Driver Assistance Systems (ADAS) face limitations in reducing false positive rates in diverse road environments due to the restricted performance of embedded vehicle systems, which hampers the effectiveness of image recognition techniques used for target object detection.
Innovation Solution
A target object detection system comprising a detection unit and a machine learning unit that uses weak and strong classifier parameters for classification, with the machine learning unit performing adaptive learning by updating weak classifier parameters when discrepancies occur, allowing for real-time operations and rapid collection of learning samples to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning algorithms are used for image recognition in embedded vehicle systems, then detection accuracy can be improved, but the system performance is limited due to restricted computational resources
Solution Approach 1:
The patent divides the machine learning system into two distinct parts: a detection unit with weak classifier parameters for real-time operation and a training unit with strong classifier parameters for offline optimization. This segmentation allows the embedded system to maintain low computational complexity while achieving high detection accuracy through the coordinated work of both units
Solution Approach 2:
The training unit performs preliminary learning operations offline to generate optimized weak classifier parameters, which are then deployed to the detection unit. This preliminary action allows the resource-constrained embedded system to benefit from complex machine learning without performing the computationally intensive training process in real-time
2Reliability
If more learning samples are collected for training, then false positive rate can be reduced, but labor-intensive auxiliary determination and tag classification are required
Solution Approach 1:
The system implements self-service learning by automatically collecting images from the detection unit and using them as training samples without requiring manual labeling. The training unit autonomously processes these samples to generate improved weak classifier parameters, eliminating the need for labor-intensive auxiliary determination and tag classification while continuously reducing the false positive rate
3Speed
If real-time detection is performed using weak classifier parameters, then processing speed is maintained, but detection accuracy is limited compared to strong classifier parameters
Solution Approach 1:
The system dynamically adapts the weak classifier parameters through continuous learning from the training unit. While the detection unit operates in real-time with lightweight weak classifiers, the parameters are periodically updated based on offline training results, allowing the system to maintain both high processing speed and improved detection accuracy over time
Data Source
AI summary
A target object detection system includes a detection unit and a machine learning unit. The detection unit obtains an image and outputs a notification signal upon determining that a portion of the image resembles a target object by performing a classification operation. The machine learning unit performs a training operation using the image to obtain new weak classifier parameters for update in the detection unit when a determination made using weak classifier parameters used in the classification operation of the detection unit differs from one made using strong classifier parameters.