A Study on Deep Convolutional Neural Networks to Enhance Object Recognition Systems

Abstract

In recent years, deep convolutional neural networks (DCNNs) have significantly advanced the field of object recognition, achieving unprecedented accuracy and efficiency. This paper presents a comprehensive study on the application of DCNNs to enhance object recognition systems, focusing on their architectural innovations, training methodologies, and performance improvements. We explore various DCNN architectures, including traditional models like AlexNet and VGG, as well as more recent advancements such as ResNet and EfficientNet. Our study examines the impact of different network depths, layer configurations, and regularization techniques on recognition accuracy. Additionally, we investigate the role of transfer learning and data augmentation in mitigating overfitting and improving generalization. Through extensive experiments on benchmark datasets, we analyze the strengths and limitations of current DCNN approaches, offering insights into their practical deployment in real-world applications. The findings highlight the potential of DCNNs to significantly enhance object recognition systems, paving the way for more robust and scalable solutions in computer vision.