A Study on Real-time Object Detection using Deep Learning

TL;DR

This paper reviews deep learning algorithms for real-time object detection, discussing models, datasets, applications, and comparing strategies to improve accuracy and efficiency in various domains.

Contribution

It provides a comprehensive overview of deep learning-based object detection methods, benchmark datasets, and comparative studies, along with challenges and future research directions.

Findings

Deep learning algorithms significantly enhance real-time object detection accuracy.

Benchmark datasets are crucial for evaluating model performance.

Various strategies impact detection speed and precision.

Abstract

Object detection has compelling applications over a range of domains, including human-computer interfaces, security and video surveillance, navigation and road traffic monitoring, transportation systems, industrial automation healthcare, the world of Augmented Reality (AR) and Virtual Reality (VR), environment monitoring and activity identification. Applications of real time object detection in all these areas provide dynamic analysis of the visual information that helps in immediate decision making. Furthermore, advanced deep learning algorithms leverage the progress in the field of object detection providing more accurate and efficient solutions. There are some outstanding deep learning algorithms for object detection which includes, Faster R CNN(Region-based Convolutional Neural Network),Mask R-CNN, Cascade R-CNN, YOLO (You Only Look Once), SSD (Single Shot Multibox Detector), RetinaNet etc. This article goes into great detail on how deep learning algorithms are used to enhance real time object recognition. It provides information on the different object detection models available, open benchmark datasets, and studies on the use of object detection models in a range of applications. Additionally, controlled studies are provided to compare various strategies and produce some illuminating findings. Last but not least, a number of encouraging challenges and approaches are offered as suggestions for further investigation in both relevant deep learning approaches and object recognition.