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Exploiting Concepts of Instance Segmentation to Boost Detection in Challenging Environments
Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgarage, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.ORCID iD: 0000-0003-0456-6493
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
Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.
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2022 (English)In: Sensors, E-ISSN 1424-8220, Vol. 22, no 10, article id 3703Article in journal (Refereed) Published
Abstract [en]

In recent years, due to the advancements in machine learning, object detection has become a mainstream task in the computer vision domain. The first phase of object detection is to find the regions where objects can exist. With the improvements in deep learning, traditional approaches, such as sliding windows and manual feature selection techniques, have been replaced with deep learning techniques. However, object detection algorithms face a problem when performed in low light, challenging weather, and crowded scenes, similar to any other task. Such an environment is termed a challenging environment. This paper exploits pixel-level information to improve detection under challenging situations. To this end, we exploit the recently proposed hybrid task cascade network. This network works collaboratively with detection and segmentation heads at different cascade levels. We evaluate the proposed methods on three complex datasets of ExDark, CURE-TSD, and RESIDE, and achieve a mAP of 0.71, 0.52, and 0.43, respectively. Our experimental results assert the efficacy of the proposed approach.

Place, publisher, year, edition, pages
MDPI, 2022. Vol. 22, no 10, article id 3703
Keywords [en]
object detection, challenging environments, low-light, complex environments, deep neural networks, computer vision
National Category
Information Systems, Social aspects Aerospace Engineering Computer Sciences
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-90933DOI: 10.3390/s22103703ISI: 000801434100001PubMedID: 35632112Scopus ID: 2-s2.0-85129857621OAI: oai:DiVA.org:ltu-90933DiVA, id: diva2:1663997
Note

Validerad;2022;Nivå 2;2022-06-03 (joosat);

Funder: European project INFINITY (883293).

Available from: 2022-06-03 Created: 2022-06-03 Last updated: 2022-06-07Bibliographically approved

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Liwicki, Marcus

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