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A Study on Indirect Performance Parameters of Object Detection

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Abstract

Object detection is one of the inevitable tasks in the technological world. When the world started to rely entirely on technological intervention for almost all the tasks, different sectors started to implant artificial intelligence for precise decision making. Object detection is one among the category, which showed its applications in various domains including health care, military and anomaly detection, etc. Since there are many review on object detection, we focus only on the methods which are less expressed but indirectly have a significant performance gain. Notwithstanding, we review predominant methods of object detection including the pre-deep learning era. From the review, we are able to conclude indirect performance parameters of object detector has a significant impact on their performance for different problem scenarios. Finally, we also highlight the best characteristic of object detection in various applications.

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Authors and Affiliations

  1. Department of Computer Science, Central University of Tamil Nadu, Thiruvarur, 610104, India

    Enoch Arulprakash & A. Martin

  2. Department of Computer Science, St. Joseph’s College of Arts and Science, Cuddalore, 607001, India

    T. Miranda Lakshmi

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  1. Enoch Arulprakash
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  2. A. Martin
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This article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K N and M. Shivakumar.

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Arulprakash, E., Martin, A. & Lakshmi, T.M. A Study on Indirect Performance Parameters of Object Detection. SN COMPUT. SCI. 3, 386 (2022). https://doi.org/10.1007/s42979-022-01277-9

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