{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:31:22Z","timestamp":1772253082017,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T00:00:00Z","timestamp":1652313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European project INFINITY","award":["883293"],"award-info":[{"award-number":["883293"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s22103703","type":"journal-article","created":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T23:08:36Z","timestamp":1652396916000},"page":"3703","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Exploiting Concepts of Instance Segmentation to Boost Detection in Challenging Environments"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0456-6493","authenticated-orcid":false,"given":"Khurram Azeem","family":"Hashmi","sequence":"first","affiliation":[{"name":"Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany"},{"name":"Mindgarage, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany"},{"name":"German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alain","family":"Pagani","sequence":"additional","affiliation":[{"name":"German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4029-6574","authenticated-orcid":false,"given":"Marcus","family":"Liwicki","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Lulea University of Technology, 971 87 Lulea, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Didier","family":"Stricker","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany"},{"name":"German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0536-6867","authenticated-orcid":false,"given":"Muhammad Zeshan","family":"Afzal","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany"},{"name":"Mindgarage, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany"},{"name":"German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dai, J., He, K., and Sun, J. 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