• Laser & Optoelectronics Progress
  • Vol. 59, Issue 8, 0815011 (2022)
Junwen Liu1, Yongjun Zhang1、*, Zhi Li1, Yong Zhao2, Xinyu Ran1, Zhongwei Cui3, and Mengjia Niu1
Author Affiliations
  • 1Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, College of Computer Science and Technology, Guizhou University, Guiyang , Guizhou 550025, China
  • 2School of Information Engineering, Peking University Shenzhen Graduate School, Shenzhen , Guangdong 518055, China
  • 3Big Data Science and Intelligent Engineering Research Institute, Guizhou Education University, Guiyang , Guizhou 550018, China
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    DOI: 10.3788/LOP202259.0815011 Cite this Article Set citation alerts
    Junwen Liu, Yongjun Zhang, Zhi Li, Yong Zhao, Xinyu Ran, Zhongwei Cui, Mengjia Niu. Head Detection Based on RDM-YOLOv3[J]. Laser & Optoelectronics Progress, 2022, 59(8): 0815011 Copy Citation Text show less
    References

    [1] Li N, Wu Y Y, Liu Y et al. Pedestrian attribute recognition algorithm based on multi-scale attention network[J]. Laser & Optoelectronics Progress, 58, 0410025(2021).

    [2] Tian Y C, Dehghan A, Shah M. On detection, data association and segmentation for multi-target tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41, 2146-2160(2019).

    [3] Yao H T, Zhang S L, Hong R C et al. Deep representation learning with part loss for person re-identification[J]. IEEE Transactions on Image Processing, 28, 2860-2871(2019).

    [4] Yu C Y, Xu Y, Gou L S et al. Crowd counting based on single-column deep spatiotemporal convolutional neural network[J]. Laser & Optoelectronics Progress, 58, 0810011(2021).

    [5] Zhang T, Zhang L. Multiscale feature fusion-based object detection algorithm[J]. Laser & Optoelectronics Progress, 58, 0215003(2021).

    [6] Ballotta D, Borghi G, Vezzani R et al. Fully convolutional network for head detection with depth images[C], 752-757(2018).

    [7] Shami M B, Maqbool S, Sajid H et al. People counting in dense crowd images using sparse head detections[J]. IEEE Transactions on Circuits and Systems for Video Technology, 29, 2627-2636(2019).

    [8] Zhang J J, Liu Y T, Li R C et al. End-to-end spatial attention network with feature mimicking for head detection[C], 199-206(2020).

    [9] Vu T H, Osokin A, Laptev I. Context-aware CNNs for person head detection[C], 2893-2901(2015).

    [10] Girshick R, Donahue J, Darrell T et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C], 580-587(2014).

    [11] Gao S H, Cheng M M, Zhao K et al. Res2Net: a new multi-scale backbone architecture[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 652-662(2021).

    [12] Hariharan B, Arbeláez P, Girshick R et al. Hypercolumns for object segmentation and fine-grained localization[C], 447-456(2015).

    [13] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9905, 21-37(2016).

    [14] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C], 779-788(2016).

    [15] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

    [16] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection[C], 936-944(2017).

    [17] Cheng B W, Wei Y C, Shi H H et al. Revisiting RCNN: on awakening the classification power of faster RCNN[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11219, 473-490(2018).

    [18] Raj R J S, Shobana S J, Pustokhina I V et al. Optimal feature selection-based medical image classification using deep learning model in internet of medical things[J]. IEEE Access, 8, 58006-58017(2020).

    [19] Huang G, Liu Z, van der Maaten L et al. Densely connected convolutional networks[C], 2261-2269(2017).

    [20] Fan Q, Zhuo W, Tang C K et al. Few-shot object detection with attention-RPN and multi-relation detector[C], 4012-4021(2020).

    [21] Wang P Q, Chen P F, Yuan Y et al. Understanding convolution for semantic segmentation[C], 1451-1460(2018).

    [22] Everingham M, Eslami S M A, Gool L et al. The pascal visual object classes challenge: a retrospective[J]. International Journal of Computer Vision, 111, 98-136(2015).

    [23] Vora A, Chilaka V. FCHD: fast and accurate head detection in crowded scenes[EB/OL]. https://arxiv.org/abs/1809.08766v3

    [24] Li W, Li H L, Wu Q B et al. HeadNet: an end-to-end adaptive relational network for head detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 30, 482-494(2020).

    [25] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

    [26] Ranjan R, Patel V M, Chellappa R. A deep pyramid deformable part model for face detection[C], 1-8(2015).

    [27] Farhadi M, Yang Y Z. TKD: temporal knowledge distillation for active perception[C], 942-951(2020).

    Junwen Liu, Yongjun Zhang, Zhi Li, Yong Zhao, Xinyu Ran, Zhongwei Cui, Mengjia Niu. Head Detection Based on RDM-YOLOv3[J]. Laser & Optoelectronics Progress, 2022, 59(8): 0815011
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