Object Detection Using Deep Learning, CNNs and Vision Transformers: A Review

Detecting objects remains one of computer vision and image understanding applications' most fundamental and challenging aspects. Significant advances in object detection have been achieved through improved object representation and the use of deep neural network models. This paper examines more closely how object detection has evolved in the era of deep learning over the past years. We present a literature review on various state-of-the-art object detection algorithms and the underlying concepts behind these methods. We classify these methods into three main groups: anchor-based, anchor-free, and transformer-based detectors. Those approaches are distinct in the way they identify objects in the image. We discuss the insights behind these algorithms and experimental analyses to compare quality metrics, speed/accuracy tradeoffs, and training methodologies. The survey compares the major convolutional neural networks for object detection. It also covers the strengths and limitations of each object detector model and draws significant conclusions. We provide simple graphical illustrations summarising the development of object detection methods under deep learning. Finally, we identify where future research will be conducted.

Automated summary

This paper examines the evolution of object detection in the era of deep learning, reviews various state-of-the-art algorithms and their underlying concepts, and classifies them into anchor-based, anchor-free, and transformer-based detectors. The paper discusses the insights behind these algorithms and provides experimental analyses comparing quality metrics, speed/accuracy trade-offs, and training methodologies. Additionally, it compares major convolutional neural networks for object detection, highlights the strengths and limitations of each model, and summarizes the development of object detection methods under deep learning through simple graphical illustrations. Finally, the paper identifies future research directions.