Performance analysis of deep learning-based object detection algorithms on COCO benchmark: a comparative study

Abstract This paper thoroughly explores the role of object detection in smart cities, specifically focusing on advancements in deep learning-based methods. Deep learning models gain popularity for their autonomous feature learning, surpassing traditional approaches. Despite progress, challenges rema...
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Autor*in:	Jiya Tian [verfasserIn] Qiangshan Jin [verfasserIn] Yizong Wang [verfasserIn] Jie Yang [verfasserIn] Shuping Zhang [verfasserIn] Dengxun Sun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Object detection Deep learning Performance analysis Accuracy COCO dataset

Übergeordnetes Werk:	In: Journal of Engineering and Applied Science - SpringerOpen, 2021, 71(2024), 1, Seite 18
Übergeordnetes Werk:	volume:71 ; year:2024 ; number:1 ; pages:18

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1186/s44147-024-00411-z

Katalog-ID:	DOAJ095737839

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Deep learning models gain popularity for their autonomous feature learning, surpassing traditional approaches. Despite progress, challenges remain, such as achieving high accuracy in urban scenes and meeting real-time requirements. The study aims to contribute by analyzing state-of-the-art deep learning algorithms, identifying accurate models for smart cities, and evaluating real-time performance using the Average Precision at Medium Intersection over Union (IoU) metric. The reported results showcase various algorithms’ performance, with Dynamic Head (DyHead) emerging as the top scorer, excelling in accurately localizing and classifying objects. Its high precision and recall at medium IoU thresholds signify robustness. The paper suggests considering the mean Average Precision (mAP) metric for a comprehensive evaluation across IoU thresholds, if available. Despite this, DyHead stands out as the superior algorithm, particularly at medium IoU thresholds, making it suitable for precise object detection in smart city applications. The performance analysis using Average Precision at Medium IoU is reinforced by the Average Precision at Low IoU (APL), consistently depicting DyHead’s superiority. These findings provide valuable insights for researchers and practitioners, guiding them toward employing DyHead for tasks prioritizing accurate object localization and classification in smart cities. 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callnumber-first	T - Technology
author	Jiya Tian
spellingShingle	Jiya Tian misc TA1-2040 misc Object detection misc Deep learning misc Performance analysis misc Accuracy misc COCO dataset misc Engineering (General). Civil engineering (General) Performance analysis of deep learning-based object detection algorithms on COCO benchmark: a comparative study
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topic_title	TA1-2040 Performance analysis of deep learning-based object detection algorithms on COCO benchmark: a comparative study Object detection Deep learning Performance analysis Accuracy COCO dataset
topic	misc TA1-2040 misc Object detection misc Deep learning misc Performance analysis misc Accuracy misc COCO dataset misc Engineering (General). Civil engineering (General)
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title_sort	performance analysis of deep learning-based object detection algorithms on coco benchmark: a comparative study
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title_auth	Performance analysis of deep learning-based object detection algorithms on COCO benchmark: a comparative study
abstract	Abstract This paper thoroughly explores the role of object detection in smart cities, specifically focusing on advancements in deep learning-based methods. Deep learning models gain popularity for their autonomous feature learning, surpassing traditional approaches. Despite progress, challenges remain, such as achieving high accuracy in urban scenes and meeting real-time requirements. The study aims to contribute by analyzing state-of-the-art deep learning algorithms, identifying accurate models for smart cities, and evaluating real-time performance using the Average Precision at Medium Intersection over Union (IoU) metric. The reported results showcase various algorithms’ performance, with Dynamic Head (DyHead) emerging as the top scorer, excelling in accurately localizing and classifying objects. Its high precision and recall at medium IoU thresholds signify robustness. The paper suggests considering the mean Average Precision (mAP) metric for a comprehensive evaluation across IoU thresholds, if available. Despite this, DyHead stands out as the superior algorithm, particularly at medium IoU thresholds, making it suitable for precise object detection in smart city applications. The performance analysis using Average Precision at Medium IoU is reinforced by the Average Precision at Low IoU (APL), consistently depicting DyHead’s superiority. These findings provide valuable insights for researchers and practitioners, guiding them toward employing DyHead for tasks prioritizing accurate object localization and classification in smart cities. Overall, the paper navigates through the complexities of object detection in urban environments, presenting DyHead as a leading solution with robust performance metrics.
abstractGer	Abstract This paper thoroughly explores the role of object detection in smart cities, specifically focusing on advancements in deep learning-based methods. Deep learning models gain popularity for their autonomous feature learning, surpassing traditional approaches. Despite progress, challenges remain, such as achieving high accuracy in urban scenes and meeting real-time requirements. The study aims to contribute by analyzing state-of-the-art deep learning algorithms, identifying accurate models for smart cities, and evaluating real-time performance using the Average Precision at Medium Intersection over Union (IoU) metric. The reported results showcase various algorithms’ performance, with Dynamic Head (DyHead) emerging as the top scorer, excelling in accurately localizing and classifying objects. Its high precision and recall at medium IoU thresholds signify robustness. The paper suggests considering the mean Average Precision (mAP) metric for a comprehensive evaluation across IoU thresholds, if available. Despite this, DyHead stands out as the superior algorithm, particularly at medium IoU thresholds, making it suitable for precise object detection in smart city applications. The performance analysis using Average Precision at Medium IoU is reinforced by the Average Precision at Low IoU (APL), consistently depicting DyHead’s superiority. These findings provide valuable insights for researchers and practitioners, guiding them toward employing DyHead for tasks prioritizing accurate object localization and classification in smart cities. Overall, the paper navigates through the complexities of object detection in urban environments, presenting DyHead as a leading solution with robust performance metrics.
abstract_unstemmed	Abstract This paper thoroughly explores the role of object detection in smart cities, specifically focusing on advancements in deep learning-based methods. Deep learning models gain popularity for their autonomous feature learning, surpassing traditional approaches. Despite progress, challenges remain, such as achieving high accuracy in urban scenes and meeting real-time requirements. The study aims to contribute by analyzing state-of-the-art deep learning algorithms, identifying accurate models for smart cities, and evaluating real-time performance using the Average Precision at Medium Intersection over Union (IoU) metric. The reported results showcase various algorithms’ performance, with Dynamic Head (DyHead) emerging as the top scorer, excelling in accurately localizing and classifying objects. Its high precision and recall at medium IoU thresholds signify robustness. The paper suggests considering the mean Average Precision (mAP) metric for a comprehensive evaluation across IoU thresholds, if available. Despite this, DyHead stands out as the superior algorithm, particularly at medium IoU thresholds, making it suitable for precise object detection in smart city applications. The performance analysis using Average Precision at Medium IoU is reinforced by the Average Precision at Low IoU (APL), consistently depicting DyHead’s superiority. These findings provide valuable insights for researchers and practitioners, guiding them toward employing DyHead for tasks prioritizing accurate object localization and classification in smart cities. Overall, the paper navigates through the complexities of object detection in urban environments, presenting DyHead as a leading solution with robust performance metrics.
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title_short	Performance analysis of deep learning-based object detection algorithms on COCO benchmark: a comparative study
url	https://doi.org/10.1186/s44147-024-00411-z https://doaj.org/article/39fe360661754ff99caefece7b6fc597 https://doaj.org/toc/1110-1903 https://doaj.org/toc/2536-9512
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Performance analysis of deep learning-based object detection algorithms on COCO benchmark: a comparative study

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