Adaptive Neural Network Training for Crossroad Vehicle Recognition
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Solution Overview
Problem
Existing vehicle recognition systems at intelligent transport crossroads suffer from low accuracy and poor recognition performance due to manually trained models.
Innovation Solution
A vehicle recognition method utilizing a neural network to automatically train a subnetwork model through adaptive constraint conditions, incorporating reward parameters and constraint condition values to optimize the model, and selecting an optimal network model for improved recognition precision and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a manually pre-trained model is used for vehicle recognition, then the system is simple to implement, but the recognition accuracy and performance are low
Solution Approach 1:
The system performs automatic model training and optimization without manual intervention. The neural network automatically learns from training data, performs self-adjustment of parameters, and selects optimal models through automated evaluation metrics, eliminating the need for manual model training while achieving high recognition accuracy
Solution Approach 2:
The system dynamically adjusts training parameters including learning rates, batch sizes, and optimization hyperparameters during the training process. It modifies model architecture parameters and training configuration parameters to optimize recognition performance while managing training complexity
2Productivity
If manual model training is performed, then the process is controllable, but the training time and resource consumption are high
Solution Approach 1:
The system implements automated partial training strategies where models are trained on subsets of data initially, then progressively expanded. Automatic hyperparameter tuning performs exhaustive search only on critical parameters while using defaults for others, reducing overall training time while maintaining effectiveness
Solution Approach 2:
The system uses automated evaluation metrics and performance feedback to continuously monitor training progress. This feedback mechanism allows the system to automatically adjust training strategies, stop training when optimal performance is achieved, and allocate resources efficiently without manual intervention
Data Source
AI summary
A vehicle recognition method includes: acquiring a training set including multiple vehicle images which includes vehicles at a designated crossroad and corresponding vehicle information; performing learning and sampling by a neural network to obtain a subnetwork model and determining same as a current model, and training the current model by using the training set to obtain reward parameters; calculating a constraint condition value of the current model, calculating a reward value on the basis of the constraint condition value and the reward parameters, updating trainable parameters of the neural network by using the reward value, and returning to execute the operation of obtaining the subnetwork model until a preset condition is reached; after the preset condition is reached, selecting an optimal network model; and inputting a vehicle picture to be recognized into the optimal network model, to obtain vehicle information of the vehicle picture to be recognized.