Quantitative Analysis of Deep Learning-Based Object Detection Models

The rise of convolutional networks in computer vision, especially for generic object detection, has led to the emergence of a myriad of efficient and precise object detection models. Typically, deep learning-driven object detectors operate in two phases: initially, they utilize convolutional networks to extract compact feature embeddings from images; subsequently, these embeddings are used to pinpoint localized object positions. Rooted in convolutional networks, these generic object detection models have the capability to learn from vast datasets that comprise hundreds of thousands of images with thousands of objects. This vast data training gives them unparalleled generalization capabilities, setting them apart from traditional methods. With the swift pace of research, new object detection models are frequently unveiled, each striving for state-of-the-art performance on renowned benchmarks. Given the abundance of viable models, selecting the optimal one can be a daunting task. In this paper, we offer a succinct overview of widely recognized object detectors, emphasizing their architectural distinctions, and presenting a quantitative comparison in terms of accuracy and inference speeds using the popular 2017 Common Objects in Context dataset.