GIRSHICK R. Fast R-CNN[C]//Proceeding of the IEEE International Conference on Computer Vision, December 11–18, 2015, Santiago, Chile. New York: IEEE, 2015: 1440–1448.
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, 2017, 39(6): 1137–1149.
DAI J F, LI Y, HE K M, et al. R-FCN object detection via region-based fully convolution networks[C]//Proceeding of the 30th Annual Conference on Neural Information Processing Systems, December 5–10, 2016, Barcelona, Spain. Neural Information Processing Systems Foundation, 2016: 379–387.
HE K M, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN[C]//Proceeding of the IEEE International Conference on Computer Vision, October 22–29, 2017, Venice, Italy. New York: IEEE, 2017: 2980–2988.
FANG L P, HE H J, ZHOU G M. Research overview of object detection methods[J]. Computer engineering and applications, 2018, 54(13): 11–18. (in Chinese)
SHAO Y H, ZHANG D, CHU H Y, et al. A review of YOLO object detection based on deep learning[J]. Journal of electronics & information technology, 2022, 44(10): 3697–3708. (in Chinese)
LI J Y, YANG J, KONG B, et al. Multi-scale vehicle-pedestrian detection algorithm based on attention mechanism[J]. Optics and precision engineering, 2021, 29(06): 1448–1458. (in Chinese)
MENG L X. Research on vehicle-pedestrian detection method based on deep learning[D]. Taiyuan: North University of China, 2021. (in Chinese)
GUO Z J, LI J Y, QI H J, et al. Detection algorithm for infrared pedestrian and vehicle based on the improved YOLOv4[J]. Laser & infrared, 2023, 53(4): 607–614. (in Chinese)
ZHOU H P, WANG J, SUN K L. Pedestrian detection algorithm based on improved YOLOv4-tiny[J]. Radio communications technology, 2021, 47(4): 474–480. (in Chinese)
YI X, SONG Y H, ZHANG Y L. Enhanced darknet53 combine MLFPN based real-time defect detection in steel surface[J]. Chinese Conference on Pattern Recognition and Computer Vision (PRCV), October 16–18, 2020, Nanjing, China. Berlin, Heidelberg: Springer Science and Business Media Deutschland GmbH, 2020: 303–314.
LIN T Y, DOLLAR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, July 21–26, 2017, Honolulu, USA. New York: IEEE, 2017: 936–944.
WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//15th European Conference on Computer Vision, September 8–14, 2018, Munich, Germany. Berlin, Heidelberg: Springer Verlag, 2018: 3–19.
TAN M M, PANG RM, LEQ V. EfficientDet: scalable and efficient object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 14–19, 2020, Virtual. New York: IEEE, 2020: 10778–10787.
CHIEN Y W, HONG Y M L, YEH I H, et al. CSPNet: a new backbone that can enhance learning capability of CNN[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 14–19, 2020, Virtual. New York: IEEE, 2020: 1571–1580.
Comments (0)