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Exploiting Concepts of Instance Segmentation to Boost Detection in Challenging Environments

Sensors (Basel, Switzerland), 2022-05, Vol.22 (10), p.3703 [Peer Reviewed Journal]

COPYRIGHT 2022 MDPI AG ;2022 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. ;2022 by the authors. 2022 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s22103703 ;PMID: 35632112

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  • Title:
    Exploiting Concepts of Instance Segmentation to Boost Detection in Challenging Environments
  • Author: 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 ; low-light ; Machine Learning ; Machine vision ; Maskininlärning ; object detection ; Object recognition ; Performance evaluation ; Segmentation
  • Is Part Of: Sensors (Basel, Switzerland), 2022-05, Vol.22 (10), p.3703
  • Description: 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.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s22103703
    PMID: 35632112
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    PubMed Central
    SWEPUB Freely available online
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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