Student Neural Network Knowledge Distillation for Object Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Deep neural networks struggle to detect objects in nonideal imaging conditions such as low light or obscured atmospheric conditions, which can lead to reduced performance due to low contrast pixel values between objects and backgrounds, increasing system complexity and computational resources when additional sensors are added.
Innovation Solution
Implementing a teacher/student training method for deep neural networks, where a teacher neural network is trained with multiple sensor types and then uses knowledge distillation or adversarial training to transfer knowledge to a student neural network that operates solely on video data, reducing computational requirements while enhancing performance in challenging conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional sensors are added to improve object detection in nonideal imaging conditions, then detection reliability is improved, but device complexity and computational resources increase
Solution Approach 1:
The patent creates a virtual copy of multi-sensor data through knowledge distillation. The teacher network processes multiple sensor inputs and the student network learns to replicate this behavior using only video data, effectively copying the multi-sensor detection capability without physical additional sensors
Solution Approach 2:
The teacher neural network serves as an intermediary that bridges the gap between limited video data and ideal multi-sensor detection. It processes video data and generates predictions that guide the student network, mediating the transformation of single-sensor input into multi-sensor equivalent performance
2Reliability
If additional sensors are added to improve object detection in nonideal imaging conditions, then detection reliability is improved, but computational resources increase
Solution Approach 1:
The student network copies the detection performance of the teacher network through knowledge distillation, achieving the same reliability with lighter computational requirements. The student network is trained to replicate teacher predictions on video data alone, eliminating the need for computationally intensive multi-sensor processing
Solution Approach 2:
The patent replaces expensive, resource-intensive multi-sensor processing with a lightweight student network that uses only video data. The student network is a simplified, more efficient model that sacrifices nothing in terms of detection reliability while consuming fewer computational resources
3Productivity
If deep neural networks process video data in nonideal imaging conditions, then object detection is performed, but detection precision deteriorates due to low contrast pixel values
Solution Approach 1:
The teacher network is pre-trained on multi-sensor data to learn robust detection patterns that work well in nonideal conditions. This preliminary training equips the teacher with knowledge that helps it generate accurate predictions even when processing only video data, which then guides the student network's learning
Data Source
AI summary
A computer that includes a processor and a memory, the memory including instructions executable by the processor to receive an image in a first neural network that outputs a first prediction based on the image, wherein weights applied to layers in the first neural network are determined by minimizing a sum of a first loss function and a second loss function. The first loss function can be determined from the first features determined in the first neural network trained to output a first prediction and from second features determined in a second neural network trained to output a second prediction. The second loss function can be determined based on comparing the first prediction to ground truth. The first prediction can be output.