Transfer Learning Neural Network Scene Adaptation
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Solution Overview
Problem
Current neural networks for object recognition require extensive training data to adapt to specific scenes, making scene-specific training cumbersome and resource-intensive, especially in surveillance applications where variations in light, vegetation, and weather conditions pose challenges.
Innovation Solution
Implementing transfer learning with a smart generation of annotated images using multiple object recognition algorithms to filter and adapt a pre-trained neural network to a specific scene, reducing the need for extensive data collection and processing capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive training data is collected and processed to train neural networks from scratch for specific scenes, then object recognition accuracy is improved, but time consumption and processing capacity requirements increase significantly
Solution Approach 1:
The patent applies preliminary action by using a pre-trained neural network that has already been trained on extensive general data before being adapted to the specific scene. This pre-training phase is completed in advance, allowing the system to leverage existing knowledge rather than starting from scratch, thus reducing the time required for scene-specific training while maintaining high recognition accuracy
Solution Approach 2:
The patent uses copying by taking a pre-trained neural network model and adapting it to the specific scene through transfer learning. Instead of creating a new model from scratch, the system copies the learned features from the pre-trained model and fine-tunes them for the specific scene, significantly reducing training time and processing requirements while preserving accuracy
2Measurement precision
If extensive training data is collected and processed to train neural networks from scratch for specific scenes, then object recognition accuracy is improved, but processing capacity requirements increase significantly
Solution Approach 1:
The pre-trained neural network has already performed the computationally intensive task of learning from extensive general data in advance. When adapting to a specific scene, the system only needs to perform fine-tuning with much lower processing capacity requirements, as the foundational representations have already been established through preliminary training on general data
Solution Approach 2:
By copying the pre-trained model's learned representations and feature extractors, the system avoids the need to recompute and relearn these fundamental patterns from scratch. This copying approach allows the system to achieve high accuracy with significantly reduced processing capacity, as only the adaptation to specific scene characteristics requires computational resources
3Loss of time
If a pre-trained neural network is used without scene-specific training, then processing time and capacity are reduced, but object recognition accuracy for the specific scene deteriorates
Solution Approach 1:
The patent applies parameter changes by adjusting the neural network's parameters through transfer learning and fine-tuning processes. The system modifies the pre-trained model's parameters to adapt to the specific scene's characteristics, such as lighting conditions, object appearances, and spatial relationships, thereby improving accuracy while maintaining the time efficiency of using a pre-trained architecture
Solution Approach 2:
The patent implements local quality by adapting the pre-trained network to handle specific local characteristics of the target scene. Instead of requiring complete retraining, the system focuses the adaptation on local features and patterns specific to the scene, such as particular object appearances, background characteristics, or lighting conditions, improving accuracy for those specific aspects while maintaining overall efficiency
Data Source
AI summary
A method of transfer learning an object recognition neural network comprises acquiring a set of image frames; determining, by a first object recognition algorithm implementing an object recognition neural network, a plurality of object recognitions in the set of image frames; determining verified object recognitions by evaluating the plurality of object recognitions by a second, different from the first, object recognition algorithm, wherein an object recognition with a positive outcome in said evaluating forms a verified object recognition; forming a training set of annotated images comprising image frames associated with the verified object recognitions; performing transfer learning of the object recognition neural network based on the training set of annotated images.