Object recognition systems often struggle to maintain accuracy in dynamic environments due to challenges such as lighting variations, occlusions, and limited training data. Traditional convolutional neural networks (CNNs) require extensive labeled datasets and lack adaptability when exposed to new conditions. This study aims to develop an adaptive object recognition framework that enhances model generalization and rapid adaptation in changing environments. By leveraging meta-learning techniques, particularly Model-Agnostic Meta-Learning (MAML), the research focuses on improving recognition performance with minimal training data. The methodology involves integrating MAML with various CNN architectures, including ResNet, EfficientNet, and MobileNet. A series of experiments were conducted to evaluate model adaptability, classification accuracy, and computational efficiency across fluctuating conditions. Performance metrics such as accuracy and response time were measured, comparing traditional CNNs with their meta-learning-enhanced counterparts. The findings demonstrate that incorporating meta-learning significantly improves object recognition accuracy. For example, ResNet models showed an accuracy increase from 78.5% to 87.2% when combined with MAML, while EfficientNet exhibited enhanced performance with reduced computational cost. The results confirm the effectiveness of meta-learning in improving adaptability without requiring extensive retraining. The novelty of this research lies in the systematic integration of meta-learning with CNNs, optimizing object recognition for real-world, dynamic scenarios. Unlike conventional models, the proposed approach enables rapid adaptation with limited data, making it highly suitable for real-time applications. The practical value of this study extends to deploying object recognition systems on resource-constrained devices such as edge AI hardware and mobile platforms. The combination of meta-learning and lightweight CNN architectures ensures both high accuracy and computational efficiency, making it applicable in fields like autonomous systems, surveillance, and robotics. Future investigations will focus on refining meta-learning optimization techniques, improving training efficiency, and extending the approach to more complex object recognition tasks in real-time, multi-object tracking environments.
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