Hyperparameter Optimization for Improving Recognition Efficiency of an Adaptive Learning System

Today, several studies have been concretized in the areas of robotics, self-driving cars, intelligent assistance systems, and so on. Developing an increasingly optimal neural network in terms of accuracy and processing speed for resource-limited systems has become a major research trend. Some research orientations include focusing on developing solutions to optimize machine learning models and learning parameters. In this study, we investigated an optimization solution for learning hyperparameters of adaptive learning systems for improving object recognition accuracy. The proposed method was developed from a framework searching a set of learning hyperparameters based on the evaluation of the previous CNN model with the collected dataset during the movement of advanced driver assistance systems (ADAS) equipment. The proposed solution consists of some major steps in a loop of adaptive learning system, such as (1) training an initial recognition model, (2) locating and receiving image data of different cases of the object during ADAS movement based on object tracking process, (3) finding optimal hyperparameters on the found dataset based on the previous recognition model, and (4) using the trained recognition model to update the current recognition model. The experimental results proved that the trained recognition model was capable of being more intelligent and displayed more diverse recognition than the previous model. The updated task for the recognition model was continuously repeated throughout the ADAS life. This approach supports and enables the recognition system to be self-adaptive and more intelligent in real life settings without manually processing.