Deep Learning Approaches for Object Detection


  • Sushma Jaiswal Guru Ghasidas Central University, Bilaspur (C.G.), India
  • Tarun Jaiswal Department of Computer Applications, NIT Raipur, Raipur, India



object detection methods, deep learning, convolutional-neural-network (CNN), computer vision, recurrent neural network (RNN)


In computer vision, object detection is a very important, exciting and mind-blowing study. Object detection work in numerous fields such as observing security, independently/autonomous driving and etc. Deep-learning based object detection techniques have developed at a very fast pace and have attracted the attention of many researchers. The main focus of the 21st century is the development of the object-detection framework, comprehensively and genuinely. In this investigation, we initially investigate and evaluate the various object detection approaches and designate the benchmark datasets. We also delivered the wide-ranging general idea of object detection approaches in an organized way. We covered the first and second stage detectors of object detection methods. And lastly, we consider the construction of these object detection approaches to give dimensions for further research.




How to Cite

Sushma Jaiswal, Tarun Jaiswal. Deep Learning Approaches for Object Detection. Artificial Intelligence Evolution [Internet]. 2020 Nov. 20 [cited 2023 Jun. 5];1(2):122-44. Available from: