YOLO-TS: A Lightweight YOLO Model for Traffic Sign Detection
Existing traffic sign detection algorithms suffer from high computational complexity and large parameter sizes, limiting their deployability. The YOLO-TS model integrates the Normalized Wasserstein Distance (NWD) with the Complete Intersection over Union (CIoU) loss function, significantly enhancing...
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description | Existing traffic sign detection algorithms suffer from high computational complexity and large parameter sizes, limiting their deployability. The YOLO-TS model integrates the Normalized Wasserstein Distance (NWD) with the Complete Intersection over Union (CIoU) loss function, significantly enhancing the detection of small traffic signs. The integration of StarBlock from StarNet into the C2f module forms the C2f-Star configuration, which simplifies the architecture. In addition, the Slimneck design paradigm is introduced into the neck network to further reduce computational demands while maintaining model accuracy. Subsequently, a Squeeze and Excitations Shared Detection Head (SESDH) is developed to integrate a squeezing and excitation attention mechanism. This design helps diminish the intricacy of the network architecture, increase attention on areas where traffic signs are presence and improve the ability of the model to indicate objects in complex surroundings. Experimental results on the CCTSDB traffic sign dataset reveal that the updated algorithm decreased the number of parameters by 35.33%, reduced the computation by 35.80% and shrunk the model size by 32.94% of the baseline YOLOv8n model while improving the mAP@50 by 1%, and the F1 score by 1.32%. Compared to other algorithms, here, a good balance between accuracy and lightweight design is achieved. |
doi_str_mv | 10.1109/ACCESS.2024.3498057 |
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The YOLO-TS model integrates the Normalized Wasserstein Distance (NWD) with the Complete Intersection over Union (CIoU) loss function, significantly enhancing the detection of small traffic signs. The integration of StarBlock from StarNet into the C2f module forms the C2f-Star configuration, which simplifies the architecture. In addition, the Slimneck design paradigm is introduced into the neck network to further reduce computational demands while maintaining model accuracy. Subsequently, a Squeeze and Excitations Shared Detection Head (SESDH) is developed to integrate a squeezing and excitation attention mechanism. This design helps diminish the intricacy of the network architecture, increase attention on areas where traffic signs are presence and improve the ability of the model to indicate objects in complex surroundings. Experimental results on the CCTSDB traffic sign dataset reveal that the updated algorithm decreased the number of parameters by 35.33%, reduced the computation by 35.80% and shrunk the model size by 32.94% of the baseline YOLOv8n model while improving the mAP@50 by 1%, and the F1 score by 1.32%. Compared to other algorithms, here, a good balance between accuracy and lightweight design is achieved.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2024.3498057</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>IEEE</publisher><subject>Attention mechanisms ; lightweight structures ; traffic sign detection ; YOLO</subject><ispartof>IEEE access, 2024, Vol.12, p.169013-169023</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0009-0007-2639-6452</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10752953$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2102,4024,27633,27923,27924,27925,54933</link.rule.ids></links><search><creatorcontrib>Liu, Yunxiang</creatorcontrib><creatorcontrib>Luo, Peng</creatorcontrib><title>YOLO-TS: A Lightweight YOLO Model for Traffic Sign Detection</title><title>IEEE access</title><addtitle>Access</addtitle><description>Existing traffic sign detection algorithms suffer from high computational complexity and large parameter sizes, limiting their deployability. The YOLO-TS model integrates the Normalized Wasserstein Distance (NWD) with the Complete Intersection over Union (CIoU) loss function, significantly enhancing the detection of small traffic signs. The integration of StarBlock from StarNet into the C2f module forms the C2f-Star configuration, which simplifies the architecture. In addition, the Slimneck design paradigm is introduced into the neck network to further reduce computational demands while maintaining model accuracy. Subsequently, a Squeeze and Excitations Shared Detection Head (SESDH) is developed to integrate a squeezing and excitation attention mechanism. This design helps diminish the intricacy of the network architecture, increase attention on areas where traffic signs are presence and improve the ability of the model to indicate objects in complex surroundings. Experimental results on the CCTSDB traffic sign dataset reveal that the updated algorithm decreased the number of parameters by 35.33%, reduced the computation by 35.80% and shrunk the model size by 32.94% of the baseline YOLOv8n model while improving the mAP@50 by 1%, and the F1 score by 1.32%. Compared to other algorithms, here, a good balance between accuracy and lightweight design is achieved.</description><subject>Attention mechanisms</subject><subject>lightweight structures</subject><subject>traffic sign detection</subject><subject>YOLO</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkF1LwzAUhoMoOOZ-gV70D3Qmzbd4M-bXoLKLzguvQpqczIy5SloQ_72tHbJzkRNeeJ8DD0LXBM8Jwfp2sVw-VtW8wAWbU6YV5vIMTQoidE45Fecn_0s0a9sd7kf1EZcTdP--Ltf5prrLFlkZtx_dNwxvNsTZa-Nhn4UmZZtkQ4guq-L2kD1AB66LzeEKXQS7b2F23FP09vS4Wb7k5fp5tVyUuesvd3ngXBNreQ0evAPGhKq5V0wqBkwIWgTtgqgVlXVw2JFCW2WJ84wKUSht6RStRq5v7M58pfhp049pbDR_QZO2xqYuuj0YhV1NgmSy9prZQikHEgP2YH1QUqieRUeWS03bJgj_PILN4NOMPs3g0xx99q2bsRUB4KQheaE5pb9FYG_V</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Liu, Yunxiang</creator><creator>Luo, Peng</creator><general>IEEE</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>DOA</scope><orcidid>https://orcid.org/0009-0007-2639-6452</orcidid></search><sort><creationdate>2024</creationdate><title>YOLO-TS: A Lightweight YOLO Model for Traffic Sign Detection</title><author>Liu, Yunxiang ; Luo, Peng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c216t-f5591aa5bededce4468b5d84784e46632f9cf6b837bfc0c129a8a1cd4366289a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Attention mechanisms</topic><topic>lightweight structures</topic><topic>traffic sign detection</topic><topic>YOLO</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Yunxiang</creatorcontrib><creatorcontrib>Luo, Peng</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Yunxiang</au><au>Luo, Peng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>YOLO-TS: A Lightweight YOLO Model for Traffic Sign Detection</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2024</date><risdate>2024</risdate><volume>12</volume><spage>169013</spage><epage>169023</epage><pages>169013-169023</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>Existing traffic sign detection algorithms suffer from high computational complexity and large parameter sizes, limiting their deployability. The YOLO-TS model integrates the Normalized Wasserstein Distance (NWD) with the Complete Intersection over Union (CIoU) loss function, significantly enhancing the detection of small traffic signs. The integration of StarBlock from StarNet into the C2f module forms the C2f-Star configuration, which simplifies the architecture. In addition, the Slimneck design paradigm is introduced into the neck network to further reduce computational demands while maintaining model accuracy. Subsequently, a Squeeze and Excitations Shared Detection Head (SESDH) is developed to integrate a squeezing and excitation attention mechanism. This design helps diminish the intricacy of the network architecture, increase attention on areas where traffic signs are presence and improve the ability of the model to indicate objects in complex surroundings. Experimental results on the CCTSDB traffic sign dataset reveal that the updated algorithm decreased the number of parameters by 35.33%, reduced the computation by 35.80% and shrunk the model size by 32.94% of the baseline YOLOv8n model while improving the mAP@50 by 1%, and the F1 score by 1.32%. Compared to other algorithms, here, a good balance between accuracy and lightweight design is achieved.</abstract><pub>IEEE</pub><doi>10.1109/ACCESS.2024.3498057</doi><tpages>11</tpages><orcidid>https://orcid.org/0009-0007-2639-6452</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Attention mechanisms lightweight structures traffic sign detection YOLO |
title | YOLO-TS: A Lightweight YOLO Model for Traffic Sign Detection |
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