CNN Fine-Tuning for Object Detection in New Environments
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Solution Overview
Problem
CNN-based sensor systems face reduced accuracy when deployed in new environments due to the limited presence of recognizable objects in standard training data sets, leading to unrecognized objects in deployment locations.
Innovation Solution
Fine-tuning the CNN by using an initial data set and captured sensor data to create an enriched data set, which includes objects from the deployment location, allowing for selective tuning of a few layers to optimize training and improve object detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a CNN is trained using a standard training data set, then the training process can be completed efficiently, but the object detection accuracy is reduced when deployed in new environments
Solution Approach 1:
The system performs preliminary action by capturing sensor data from the deployment environment before final model deployment. This captured data is then used to fine-tune the pre-trained CNN model, allowing the system to adapt to specific environmental characteristics in advance, thereby improving detection accuracy in the target environment without sacrificing training efficiency
Solution Approach 2:
The system applies parameter changes by modifying the CNN model parameters through fine-tuning using captured sensor data from the deployment environment. This involves adjusting weights and biases of the neural network layers based on local data characteristics, enabling the model to adapt to new environmental conditions while maintaining the efficiency benefits of the pre-trained architecture
2Measurement precision
If a CNN is trained using an enriched data set with captured sensor data, then object detection accuracy in new environments is improved, but the training complexity increases
Solution Approach 1:
The training process is segmented into two distinct phases: first, training with the standard training data set to establish a pre-trained model; second, fine-tuning with captured sensor data from the deployment environment. This segmentation allows the system to benefit from the efficiency of standard training while incorporating local adaptations, reducing overall complexity compared to training entirely from scratch with enriched data
Solution Approach 2:
The system applies partial action by performing fine-tuning on only certain layers of the CNN model using captured sensor data, rather than retraining the entire model. This selective approach focuses computational resources on adapting the most critical parts of the model to the deployment environment, thereby improving detection accuracy without proportionally increasing training complexity
Data Source
AI summary
A method for fine-tuning a convolutional neural network (CNN) and a sensor system based on a CNN are disclosed. The sensor system may be deployed at a deployment location. The CNN may be fine-tuned for the deployment location using sensor data, e.g., images, captured by a sensor device of the sensor system at the deployment location. The sensor data may include objects that are not present in an initial data set used for training the CNN. The sensor data and the initial data set may be input to the CNN to train the CNN and obtain fine-tuned parameters of the CNN. The CNN can thus be fine-tuned to the deployment location of the sensor system, with an increased chance of recognizing objects when using the sensor system and the CNN to recognize objects in captured sensor data.