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Deep learning for object detection: state of the art, challenges, and future directions

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Published in:JDSA
Format: Online Article RSS Article
Published: 2026
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discipline_display Engineering & Technology
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format Online Article
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institution FRELIP
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publishDate 2026
publishDateSort 2026
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spellingShingle Deep learning for object detection: state of the art, challenges, and future directions
Big data and Data science
Computer Science & IT
Engineering & Technology
sub_discipline_display Computer Science & IT
sub_discipline_facet Computer Science & IT
subject_display Big data and Data science
Computer Science & IT
Engineering & Technology
Big data and Data science
Computer Science & IT
Engineering & Technology
subject_facet Big data and Data science
Computer Science & IT
Engineering & Technology
title Deep learning for object detection: state of the art, challenges, and future directions
title_auth Deep learning for object detection: state of the art, challenges, and future directions
title_full Deep learning for object detection: state of the art, challenges, and future directions
title_fullStr Deep learning for object detection: state of the art, challenges, and future directions
title_full_unstemmed Deep learning for object detection: state of the art, challenges, and future directions
title_short Deep learning for object detection: state of the art, challenges, and future directions
title_sort deep learning for object detection: state of the art, challenges, and future directions
topic Big data and Data science
Computer Science & IT
Engineering & Technology
url https://link.springer.com/article/10.1007/s41060-026-01030-y