Abstract
Aiming at the problem of low detection accuracy of vehicle and pedestrian detection models, this paper proposes an improved you only look once v4 (YOLOv4)-tiny vehicle and pedestrian target detection algorithm. Convolutional block attention module (CBAM) is introduced into cross stage partial Darknet-53 (CSPDarknet53)-tiny module to enhance feature extraction capabilities. In addition, the cross stage partial dense block layer (CSP-DBL) module is used to replace the original simple convolutional module superposition, which compensates for the high-resolution characteristic information and further improves the detection accuracy of the network. Finally, the test results on the BDD100K traffic dataset show that the mean average precision (mAP) value of the final network of the proposed method is 88.74%, and the detection speed reaches 63 frames per second (FPS), which improves the detection accuracy of the network and meets the real-time detection speed.
Similar content being viewed by others
References
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.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare no conflict of interest.
Additional information
This work has been supported by the National Natural Science Foundation of China (Nos.61975151 and 61308120).
Rights and permissions
About this article
Cite this article
Li, J., Xu, Z. & Xu, L. Vehicle and pedestrian detection method based on improved YOLOv4-tiny. Optoelectron. Lett. 19, 623–628 (2023). https://doi.org/10.1007/s11801-023-3078-x
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11801-023-3078-x