Knowledge Distillation for Embedded Object Detection
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Solution Overview
Problem
Deep learning models for autonomous vehicles require high computational power and large capacity for effective object detection, making it challenging to apply them in real-time scenarios within the constraints of embedded systems.
Innovation Solution
The proposed solution involves an apparatus and method for building a deep learning model that includes a first deep learning model to obtain spatial feature maps and heatmaps, and a second deep learning model that performs imitation learning to mimic the first model, thereby improving object detection performance while optimizing for real-time calculations and embedded systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a deep learning model with large capacity and high computation power is used to improve object detection performance, then object detection performance is improved, but the model cannot be applied in real-time scenarios within the constraints of embedded systems
Solution Approach 1:
The patent applies knowledge distillation where a student model (second deep learning model) learns to imitate the teacher model (first deep learning model). The student model replicates the essential detection capabilities of the larger teacher model while operating at a reduced computational scale, enabling real-time performance on embedded systems without sacrificing detection accuracy
Solution Approach 2:
The patent transforms the model architecture by changing computational parameters through imitation learning. The student model uses simplified versions of the teacher model's layers and operations, adjusting model capacity parameters to match embedded system constraints while maintaining detection performance through learned transformations
2Measurement precision
If a deep learning model with large capacity is used to improve object detection performance, then object detection performance is improved, but the types of models that can be applied to embedded systems are limited
Solution Approach 1:
The knowledge distillation framework creates a student model that can be applied to various embedded systems with different computational constraints. By learning from the teacher model, the student model adapts to different hardware platforms while maintaining detection performance, thereby expanding model selection flexibility
Solution Approach 2:
The patent creates a universal student model architecture that can be deployed across multiple types of embedded systems. The imitation learning approach enables the same student model structure to serve different application scenarios and hardware constraints, enhancing adaptability
Data Source
AI summary
An apparatus for building a deep learning model for image learning includes a first deep learning model configured to obtain a first spatial feature map and a first heatmap including center information of an object belonging to an image by learning the image. The apparatus also includes a second deep learning model configured to perform imitation learning on the first deep learning model. The second deep learning model may obtain a second spatial feature map by learning the image and perform learning such that the second spatial feature map imitates the first spatial feature map. The second deep learning model may also obtain a second heatmap including a center of the object and perform learning such that the second heatmap imitates the first heatmap.