Language:

Survey and Performance Analysis of Deep Learning Based Object Detection in Challenging Environments

Sensors (Basel, Switzerland), 2021-07, Vol.21 (15), p.5116 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2021 by the authors. 2021 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s21155116 ;PMID: 34372351

Full text available

Citations Cited by

Actions
1. Add to My Research
2. Remove from My Research
3. E-mail
4. Print
5. Permalink
6. Citation
7. EasyBib
8. EndNote
9. RefWorks
10. Delicious
11. Export RIS
12. Export BibTeX

Title:
Survey and Performance Analysis of Deep Learning Based Object Detection in Challenging Environments
Author: Ahmed, Muhammad ; Hashmi, Khurram Azeem ; Pagani, Alain ; Liwicki, Marcus ; Stricker, Didier ; Afzal, Muhammad Zeshan
Subjects: Algorithms ; challenging environments ; complex environments ; computer vision ; Datasets ; Deep Learning ; deep neural networks ; Humans ; image enhancement ; Localization ; low light ; Machine Learning ; Maskininlärning ; Meteorological satellites ; Neural networks ; Neural Networks, Computer ; object detection ; Object recognition ; performance analysis ; Qualitative analysis ; Review ; Sensors ; state of the art ; Telematics
Is Part Of: Sensors (Basel, Switzerland), 2021-07, Vol.21 (15), p.5116
Description: 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.
Publisher: Switzerland: MDPI AG
Language: English
Identifier: ISSN: 1424-8220
EISSN: 1424-8220
DOI: 10.3390/s21155116
PMID: 34372351
Source: Open Access: PubMed Central
Geneva Foundation Free Medical Journals at publisher websites
AUTh Library subscriptions: ProQuest Central
MEDLINE
SWEPUB Freely available online
ROAD: Directory of Open Access Scholarly Resources
DOAJ Directory of Open Access Journals

Back to results list


INSPIRE LIBRARY - TON DUC THANG UNIVERSITY	(84-028) 37 755 057	Feedback
19 Nguyen Huu Tho St. Dist.7, HCM	thuvien@tdtu.edu.vn	Feedback

Survey and Performance Analysis of Deep Learning Based Object Detection in Challenging Environments

Searching Remote Databases, Please Wait