Title: Generic object detection in real-time images under poorly visible conditions: a systematic literature review

Authors: Perla Sunanda; Dwaram Kavitha

Addresses: Department of CSE, Jawaharlal Nehru Technological University, Ananthapuramu, Andhra Pradesh, India; Department of CSE, G. Pulla Reddy Engineering College (Autonomous), Kurnoo, Andhra Pradesh, India ' Department of CSE, G. Pulla Reddy Engineering College, Kurnool, Andhra Pradesh, India

Abstract: The invention and usage of CNN in computer vision (CV) have made object detection an emerging task to locate and identify objects in an image or video is facing challenge with poorly visible conditions. This review aims to know the research gap for detecting generic objects, to identify the frameworks needed for working with real-time images, to see the importance of image enhancement and the need for designing nighttime datasets. A systematic literature search of studies were carried out in Scopus and IEEE databases to select object detection studies specifying generic object detection, real-time images, poorly visible or lowlight conditions, image enhancement pre-processing, the type of framework or algorithms needed, and the nighttime datasets. The timeframe for the analysis was from January 2010 to the latest month of 2022. The study shows that there is an utmost need for detecting objects in nighttime or lowlight conditions.

Keywords: computer vision; CV; object detection; obstacle detection; poorly visible; low light condition; nighttime.

DOI: 10.1504/IJCVR.2024.139551

International Journal of Computational Vision and Robotics, 2024 Vol.14 No.4, pp.401 - 444

Received: 20 Apr 2022
Accepted: 27 Sep 2022
Published online: 04 Jul 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article