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Survey and Performance Analysis of Deep Learning Based Object Detection in Challenging Environments
Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgrage, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany.
Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgrage, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany .
German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0003-4029-6574
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2021 (English)In: Sensors, E-ISSN 1424-8220, Vol. 21, no 15Article, review/survey (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2021. Vol. 21, no 15
Keywords [en]
object detection, challenging environments, low light, image enhancement, complex environments, state of the art, deep neural networks, computer vision, performance analysis
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-86662DOI: 10.3390/s21155116ISI: 000682342000001PubMedID: 34372351Scopus ID: 2-s2.0-85111328296OAI: oai:DiVA.org:ltu-86662DiVA, id: diva2:1585274
Note

Validerad;2021;Nivå 2;2021-08-16 (johcin);

Finansiär: INFINITY (883293)

Available from: 2021-08-16 Created: 2021-08-16 Last updated: 2022-02-10Bibliographically approved

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