Paper Info
Title
Survey And Performance Analysis Of Deep Learning Based Object Detection In Challenging Environments
Abstract
Recent progress in deep learning has led to accurate and efficient generic object detection networks. Training of highly reliable models depends on large datasets with highly textured and rich images. However, in real-world scenarios, the performance of the generic object detection system decreases when (i) occlusions hide the objects, (ii) objects are present in low-light images, or (iii) they are merged with background information. In this paper, we refer to all these situations as challenging environments. With the recent rapid development in generic object detection algorithms, notable progress has been observed in the field of deep learning-based object detection in challenging environments. However, there is no consolidated reference to cover the state of the art in this domain. To the best of our knowledge, this paper presents the first comprehensive overview, covering recent approaches that have tackled the problem of object detection in challenging environments. Furthermore, we present a quantitative and qualitative performance analysis of these approaches and discuss the currently available challenging datasets. Moreover, this paper investigates the performance of current state-of-the-art generic object detection algorithms by benchmarking results on the three well-known challenging datasets. Finally, we highlight several current shortcomings and outline future directions.
Year
DOI
Venue
2021
10.3390/s21155116
SENSORS
Keywords
DocType
Volume
object detection, challenging environments, low light, image enhancement, complex environments, state of the art, deep neural networks, computer vision, performance analysis
Journal
21
Issue
ISSN
Citations 
15
1424-8220
1
PageRank 
References 
Authors
0.37
0
6
Name
Order
Citations
PageRank
Muhammad Ahmed110.37
Khurram Azeem Hashmi211.38
Alain Pagani353.57
Marcus Liwicki4395.14
Didier Stricker51266138.03
Muhammad Zeshan Afzal611.72