An Optimal Review of Deep Learning Algorithms for Object Detection

Computer vision, a vital facet of deep learning, seamlessly integrates object localization and classification techniques to attain precise outcomes. Deep learning, nestled within the broader realm of machine learning, leverages neural networks as its fundamental algorithm. Particularly in the domain of images and computer vision, convolutional neural networks (CNNs) reign supreme. Over the years, numerous researchers have innovated state-of-the-art models by tailoring the foundational CNN architecture in diverse manners. This paper undertakes a comparative analysis of several models, evaluating their performance based on metrics such as mean average precision (MAP) or accuracy across various datasets. Additionally, we intend to quantify their computational complexity, measured in floating-point operations (FLOPs), employing a TensorFlow profiler.