Volume 15, Number 3

Exploring Deep Learning Models for Image Recognition: A Comparative Review

  Authors

Siddhartha Nuthakki¹, Sai Kalyana pranitha buddiga² and Sonika Koganti³, ¹First Object Inc, USA, ²Independent Researcher, USA, ³Software Engineer, USA

  Abstract

Image recognition, which comes under Artificial Intelligence (AI) is a critical aspect of computer vision, enabling computers or other computing devices to identify and categorize objects within images. Among numerous fields of life, food processing is an important area, in which image processing plays a vital role, both for producers and consumers. This study focuses on the binary classification of strawberries, where images are sorted into one of two categories. We Utilized a dataset of strawberry images for this study; we aim to determine the effectiveness of different models in identifying whether an image contains strawberries. This research has practical applications in fields such as agriculture and quality control. We compared various popular deep learning models, including MobileNetV2, Convolutional Neural Networks (CNN), and DenseNet121, for binary classification of strawberry images. The accuracy achieved by MobileNetV2 is 96.7%, CNN is 99.8%, and DenseNet121 is 93.6%. Through rigorous testing and analysis, our results demonstrate that CNN outperforms the other models in this task. In the future, the deep learning models can be evaluated on a richer and larger number of images (datasets) for better/improved results.

  Keywords

Image recognition, Deep Learning, CNN, MobileNetV2, DenseNet121, Binary Classification, Strawberry Images