DCW-YOLO: Road Object Detection Algorithms for Autonomous Driving

Aiming at the problems of multiple parameters and poor detection accuracy of object detection network in automatic driving scenarios, an object detection algorithm based on improved YOLOv8 is proposed. First, a dynamic head framework is used to unify the object detection head and the attention mecha...

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Vydané v:	IEEE access Ročník 13; s. 125676 - 125688
Hlavní autori:	Ren, Hongge, Jing, Fangke, Li, Song
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Accuracy Algorithms Attention Autonomous driving Autonomous vehicles Benchmark testing Computational modeling Deep learning Feature extraction Gradient methods Heuristic algorithms Location awareness Object detection Real-time systems Roads Task analysis YOLO YOLOv8
ISSN:	2169-3536, 2169-3536
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Abstract	Aiming at the problems of multiple parameters and poor detection accuracy of object detection network in automatic driving scenarios, an object detection algorithm based on improved YOLOv8 is proposed. First, a dynamic head framework is used to unify the object detection head and the attention mechanism, and the attention mechanism is used for scale-awareness, spatial-awareness, and task-awareness, respectively, which significantly improves the representation capability of the object detection head without increasing the computational overhead. Second, the Coordinate Attention mechanism is embedded in the SPPF layer, which embeds the target's location information into the channel attention to offer more precise localization for the model, suppress irrelevant aspects, and enable greater integration of local and global characteristics. Finally, the deleterious gradients generated by low-quality examples are reduced using the Wise-IoU v3 bounding box loss function in conjunction with a dynamic non-monotonic focusing mechanism utilizing an anchor box gradient gain assignment strategy. On the challenging public dataset KITTI, the accuracy is improved by 2.1% compared to the benchmark algorithm. In addition, the excellent performance on CCTSDB2021 and VOC highlights the generalization performance of the improved model.
AbstractList	Aiming at the problems of multiple parameters and poor detection accuracy of object detection network in automatic driving scenarios, an object detection algorithm based on improved YOLOv8 is proposed. First, a dynamic head framework is used to unify the object detection head and the attention mechanism, and the attention mechanism is used for scale-awareness, spatial-awareness, and task-awareness, respectively, which significantly improves the representation capability of the object detection head without increasing the computational overhead. Second, the Coordinate Attention mechanism is embedded in the SPPF layer, which embeds the target’s location information into the channel attention to offer more precise localization for the model, suppress irrelevant aspects, and enable greater integration of local and global characteristics. Finally, the deleterious gradients generated by low-quality examples are reduced using the Wise-IoU v3 bounding box loss function in conjunction with a dynamic non-monotonic focusing mechanism utilizing an anchor box gradient gain assignment strategy. On the challenging public dataset KITTI, the accuracy is improved by 2.1% compared to the benchmark algorithm. In addition, the excellent performance on CCTSDB2021 and VOC highlights the generalization performance of the improved model.
Author	Li, Song Jing, Fangke Ren, Hongge
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Cites_doi	10.48550/arXiv.2004.10934 10.1088/1742-6596/1802/3/032073 10.1109/tpami.2019.2913372 10.1109/tvt.2021.3049805 10.1109/CVPR.2018.00913 10.1007/s10489-019-01511-7 10.1016/j.engappai.2022.104914 10.1109/jstars.2017.2694890 10.1007/978-3-030-01234-2_1 10.1109/tmm.2017.2759508 10.1109/tie.2019.2962413 10.1109/mits.2022.3201400 10.1109/tpami.2020.3032166 10.1109/tpami.2016.2577031 10.1007/s11036-021-01845-y 10.3390/s23167190 10.1007/s11263-019-01204-1 10.3389/fnbot.2023.1058723 10.1109/CVPR46437.2021.00729 10.1007/978-3-319-46448-0_2 10.3390/electronics12102323 10.1109/tits.2020.2982804 10.3390/s22103783 10.3390/s22030921 10.1007/s11263-009-0275-4 10.1016/j.compag.2023.108006 10.1109/tpami.2018.2844175 10.1155/2022/9782608 10.3390/s22093349 10.1109/tim.2022.3153997 10.1007/s11263-006-9038-7 10.3390/s23135912 10.3390/drones7050304 10.1109/access.2023.3252021 10.1109/ICCV.2019.00925 10.1109/tpami.2019.2956516 10.3390/s23115307 10.1109/CVPR46437.2021.01350 10.1016/j.autcon.2016.05.008
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References	Fu (ref9) 2017 ref13 ref15 ref14 ref10 ref17 ref16 ref19 ref18 Wang (ref36) 2019 Ge (ref40) 2021 He (ref47) 2021 ref51 ref46 ref42 ref44 ref43 ref8 ref7 Redmon (ref11) 2016 ref4 ref3 ref6 ref5 ref35 ref34 ref37 ref31 ref30 ref33 ref32 ref2 ref1 ref38 Tong (ref49) 2023 ref24 Zhang (ref48) 2021 ref23 ref26 ref25 ref20 ref22 Cao (ref41) 2019 ref21 Wang (ref39) 2022 Redmon (ref12) 2018 ref28 ref27 ref29 Wang (ref45) 2019 Zhang (ref50) 2022; 12
References_xml	– year: 2022 ident: ref39 article-title: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors publication-title: arXiv:2207.02696 – volume: 12 start-page: 12 year: 2022 ident: ref50 article-title: CCTSDB 2021: A more comprehensive traffic sign detection benchmark publication-title: Hum.-Centric Comput. Inf. Sci. – ident: ref13 doi: 10.48550/arXiv.2004.10934 – ident: ref28 doi: 10.1088/1742-6596/1802/3/032073 – ident: ref43 doi: 10.1109/tpami.2019.2913372 – year: 2023 ident: ref49 article-title: Wise-IoU: Bounding box regression loss with dynamic focusing mechanism publication-title: arXiv:2301.10051 – year: 2021 ident: ref48 article-title: Focal and efficient IOU loss for accurate bounding box regression publication-title: arXiv:2101.08158 – ident: ref20 doi: 10.1109/tvt.2021.3049805 – ident: ref37 doi: 10.1109/CVPR.2018.00913 – ident: ref30 doi: 10.1007/s10489-019-01511-7 – ident: ref14 doi: 10.1016/j.engappai.2022.104914 – year: 2017 ident: ref9 article-title: DSSD: Deconvolutional single shot detector publication-title: arXiv:1701.06659 – year: 2019 ident: ref36 article-title: CSPNet: A new backbone that can enhance learning capability of CNN publication-title: arXiv:1911.11929 – ident: ref3 doi: 10.1109/jstars.2017.2694890 – ident: ref44 doi: 10.1007/978-3-030-01234-2_1 – ident: ref4 doi: 10.1109/tmm.2017.2759508 – ident: ref27 doi: 10.1109/tie.2019.2962413 – ident: ref10 doi: 10.1109/mits.2022.3201400 – ident: ref23 doi: 10.1109/tpami.2020.3032166 – ident: ref5 doi: 10.1109/tpami.2016.2577031 – ident: ref22 doi: 10.1007/s11036-021-01845-y – ident: ref26 doi: 10.3390/s23167190 – ident: ref21 doi: 10.1007/s11263-019-01204-1 – ident: ref33 doi: 10.3389/fnbot.2023.1058723 – year: 2016 ident: ref11 article-title: YOLO9000: Better, faster, stronger publication-title: arXiv:1612.08242 – year: 2019 ident: ref41 article-title: Prime sample attention in object detection publication-title: arXiv:1904.04821 – year: 2019 ident: ref45 article-title: ECA-net: Efficient channel attention for deep convolutional neural networks publication-title: arXiv:1910.03151 – ident: ref42 doi: 10.1109/CVPR46437.2021.00729 – ident: ref8 doi: 10.1007/978-3-319-46448-0_2 – ident: ref24 doi: 10.3390/electronics12102323 – year: 2018 ident: ref12 article-title: YOLOv3: An incremental improvement publication-title: arXiv:1804.02767 – ident: ref19 doi: 10.1109/tits.2020.2982804 – ident: ref31 doi: 10.3390/s22103783 – year: 2021 ident: ref40 article-title: YOLOX: Exceeding YOLO series in 2021 publication-title: arXiv:2107.08430 – ident: ref17 doi: 10.3390/s22030921 – ident: ref51 doi: 10.1007/s11263-009-0275-4 – ident: ref25 doi: 10.1016/j.compag.2023.108006 – ident: ref6 doi: 10.1109/tpami.2018.2844175 – ident: ref18 doi: 10.1155/2022/9782608 – ident: ref29 doi: 10.3390/s22093349 – ident: ref32 doi: 10.1109/tim.2022.3153997 – ident: ref2 doi: 10.1007/s11263-006-9038-7 – ident: ref15 doi: 10.3390/s23135912 – year: 2021 ident: ref47 article-title: Alpha-IoU: A family of power intersection over union losses for bounding box regression publication-title: arXiv:2110.13675 – ident: ref16 doi: 10.3390/drones7050304 – ident: ref34 doi: 10.1109/access.2023.3252021 – ident: ref38 doi: 10.1109/ICCV.2019.00925 – ident: ref7 doi: 10.1109/tpami.2019.2956516 – ident: ref35 doi: 10.3390/s23115307 – ident: ref46 doi: 10.1109/CVPR46437.2021.01350 – ident: ref1 doi: 10.1016/j.autcon.2016.05.008
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SubjectTerms	Accuracy Algorithms Attention Autonomous driving Autonomous vehicles Benchmark testing Computational modeling Deep learning Feature extraction Gradient methods Heuristic algorithms Location awareness Object detection Real-time systems Roads Task analysis YOLO YOLOv8
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Title	DCW-YOLO: Road Object Detection Algorithms for Autonomous Driving
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