Dual Classifier Object Detection for Resource-Constrained Environments
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Solution Overview
Problem
Existing data analysis systems face challenges in efficiently identifying critical classes of objects within data structures due to limited computing resources and the risk of misclassification, particularly in applications like autonomous vehicles where accurate detection of pedestrians is crucial.
Innovation Solution
A data analysis system that includes an encoder, critical class classifier, general classifier, conditioner, and decoder, which encodes data, performs classifications, conditions the data, and decodes it to determine if the data represents an unknown or critical class object, thereby prioritizing the detection of critical class objects while managing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a comprehensive classification system is used to identify all object classes, then detection accuracy improves, but computational resources and processing time increase significantly
Solution Approach 1:
The classification system is segmented into two independent parts: a critical class classifier that identifies high-priority objects (pedestrians, vehicles) and a general classifier that identifies other objects. This segmentation allows the system to allocate computational resources selectively, focusing intensive processing on critical classes while using lighter processing for general classes, thereby resolving the contradiction between detection accuracy and computational resource consumption.
Solution Approach 2:
Different classification quality levels are applied to different object categories. Critical class objects receive high-precision classification with extensive feature analysis and multiple verification passes, while general class objects receive standard classification. This local quality differentiation ensures maximum detection accuracy for safety-critical objects without proportionally increasing computational resources for all objects uniformly.
2Reliability
If the system processes all data through comprehensive classification, then detection reliability improves, but processing time increases
Solution Approach 1:
The processing pipeline is segmented into parallel tracks: one for critical class objects and one for general class objects. Critical class data undergoes rigorous multi-stage verification to ensure reliability, while general class data processes through a faster, lighter pathway. This segmentation enables the system to maintain high reliability for critical detections without letting processing time for all data uniformly increase.
Solution Approach 2:
The critical class classifier performs preliminary screening of all input data to identify potential critical objects before they enter the general classification pipeline. This preliminary action filters and prioritizes data, ensuring that only data requiring high-reliability processing undergoes extensive analysis, while other data can be processed more quickly, thus managing processing time while maintaining reliability where it matters most.
3Measurement precision
If the system uses multiple classification models, then detection accuracy for critical classes improves, but system complexity increases
Solution Approach 1:
The system is segmented into distinct functional modules: a critical class classifier module, a general classifier module, and a unified decoder module. Each module is independently trained, optimized, and deployed. This modular segmentation achieves high detection accuracy for critical classes through specialized models while managing overall system complexity through clear separation of concerns and independent optimization of each component.
Solution Approach 2:
The critical class classification functionality is extracted as a separate, dedicated module rather than being integrated into the general classification system. This extraction allows the critical class detector to receive specialized training data and optimization focused solely on high-priority objects, achieving superior accuracy without requiring the entire system to be overly complex. The extracted module can be independently tuned and updated without affecting the general classification performance.
Data Source
AI summary
An information handling system for managing detection of objects includes a storage and a processor. The storage is for storing an encoder; a critical class classifier; a general classifier; and a decoder. The processor obtains data that may include one or more of the objects; encodes the data using the encoder to obtain encoded data; obtains a critical class classification for the encoded data using the critical class classifier; obtains a general classification for the encoded data using the general classifier; conditions the encoded data to obtain conditioned encoded data; decodes the conditioned encoded data using the decoder to obtain reconstructed data; makes a determination that the reconstructed data and the critical class classification indicate that the data is an unknown classification; classifies the data as being an unknown classification based on the determination; and performs an action set based on the unknown classification of the data.