Comparative Analysis of Various CNN Architectures in Recognizing Objects in a Classification System

Object recognition, an essential technique in computer vision, enables machines to identify and understand real-time objects and environments based on input images. The main aim of this technology is to accurately recognize image features to enable accurate object recognition. With the rapid evolution of various machine learning (ML) and deep learning (DL) algorithms, the world of image processing and computer vision has witnessed significant growth. Deep learning algorithms offer high accuracy by processing vast amounts of data, while machine learning algorithms provide flexibility in selecting the best combinations of features and classifiers for learning purposes. Convolutional Neural Network (CNN) is a widely used deep-learning model that is highly effective in classifying images. AlexNet, VGG16, ResNet and GoogleNet are some of the well-known CNN architectures used for object recognition. The paper proposes a comparative analysis of prediction accuracy between these pre-trained CNN models using transfer learning. The transfer learning approach, on the other hand, involves the fine-tuning of pre-trained models to enhance prediction accuracy in various image classification scenarios. In this paper, the CNN models are trained and tested on a dataset containing images of apples, oranges and bananas. Experimental results with a real dataset show that MobileNet V2 has the highest accuracy of 92.80%.