Deep Network Fusion for Robust Object Detection
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Solution Overview
Problem
Conventional object detection methods in visual images, such as pedestrian detection, face challenges in achieving reliable and accurate real-time identification with high false positive rates, which is critical for applications like video surveillance and advanced driver assistance systems.
Innovation Solution
A deep neural network fusion architecture that combines a primary object detector with a classification network to adjust confidence scores through a confidence scaling factor, incorporating soft rejection fusion and pixel-wise semantic segmentation for improved accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional object detection methods are used, then processing speed is maintained, but detection reliability and accuracy deteriorate with high false positive rates
Solution Approach 1:
The detection system is segmented into multiple specialized networks: a primary object detector for initial detection, a classification network for verifying object categories, and a confidence scoring network for reliability assessment. This segmentation allows each component to specialize in specific tasks, improving overall detection reliability while maintaining efficient processing through parallel operation.
Solution Approach 2:
The system performs preliminary filtering by the primary object detector before subsequent verification stages. Candidate objects are pre-identified and filtered based on initial confidence scores, reducing the computational burden on later stages and maintaining processing speed while improving reliability through multi-stage verification.
2Measurement precision
If conventional object detection methods are used, then processing speed is maintained, but detection accuracy deteriorates
Solution Approach 1:
The system implements feedback mechanisms where confidence scores from the primary detector are refined by a dedicated confidence scoring network. The classification network provides feedback on object category verification, and confidence scores are continuously adjusted based on multiple factors including object characteristics and detection context, thereby improving accuracy without significantly impacting processing speed.
Solution Approach 2:
The system adds an additional dimension of confidence scoring beyond binary detection. Instead of simple presence/absence detection, the confidence scoring network evaluates detection reliability on a continuous scale, providing nuanced accuracy measurement that improves detection precision while maintaining efficient processing through optimized neural network operations.
3Reliability
If multiple detection networks are combined, then detection reliability improves, but system complexity increases
Solution Approach 1:
Multiple detection networks are merged into a unified architecture where the primary object detector, classification network, and confidence scoring network operate in an integrated pipeline. This merging allows for coordinated operation and shared computational resources, improving detection reliability while managing system complexity through unified design and optimized data flow between components.
Solution Approach 2:
The neural network architecture is designed with multi-functional components that serve multiple purposes. The primary object detector simultaneously performs location detection and initial classification, while the confidence scoring network integrates information from multiple sources to provide comprehensive reliability assessment, reducing the need for separate specialized components and managing overall system complexity.
Data Source
AI summary
A method and apparatus are provided. The method includes receiving an image, detecting an object in the image, determining, by a primary object detector, a primary confidence detection score of the object, determining, by a classification network, a confidence scaling factor of the object, and adjusting the primary confidence detection score based on multiplying the primary confidence detection score by the confidence scaling factor.