1 |
GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580-587.
|
2 |
GIRSHICK R. Fast R-CNN[C]//Proc. of the IEEE International Conference on Computer Vision, 2015: 1440-1448.
|
3 |
REN S Q , HE K M , GIRSHICK R , et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2016, 39 (6): 1137- 1149.
|
4 |
HE K M, GKIOXARI G, DOLLAR P, et al. Mask R-CNN[C]//Proc. of the IEEE International Conference on Computer Vision, 2017: 2961-2969.
|
5 |
LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Proc. of the 14th European Conference of Computer Vision-ECCV, 2016: 21-37.
|
6 |
REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779-788.
|
7 |
REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 7263-7271.
|
8 |
GE Z, LIU S T, WANG F, et al. YOLOX: exceeding YOLO series in 2021[EB/OL]. [2023-06-03]. https://arxiv.org/abs/2107.08430.html.
|
9 |
LI C Y, LI L L, JIANG H L, et al. YOLOv6: a single-stage object detection framework for industrial applications[EB/OL]. [2023-06-03]. https://arxiv.org/abs/2209.02976.html.
|
10 |
WANG C Y, BOCHKOVSKIY A, LIAOH Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 7464-7475.
|
11 |
皮骏, 刘宇恒, 李久昊. 基于YOLO v5s的轻量化森林火灾检测算法研究[J]. 图学学报, 2023, 44 (1): 26- 32.
|
|
PI J , LIU Y H , LI J H . Research on lightweight forest fire detection algorithm based on YOLO v5s[J]. Journal of Graphics, 2023, 44 (1): 26- 32.
|
12 |
YAN F X, XU Y X. Improved target detection algorithm based on YOLO[C]//Proc. of the 4th International Conference on Robotics, Control and Automation Engineering, 2021: 21-25.
|
13 |
牛为华, 殷苗苗. 基于改进YOLO v5的道路小目标检测算法[J]. 传感技术学报, 2023, 36 (1): 36- 44.
|
|
NIU W H , YIN M M . Road small target detection algorithm based on improved YOLO v5[J]. Chinese Journal of Sensors and Actuators, 2023, 36 (1): 36- 44.
|
14 |
XU X K , FENG Z J , CAO C Q , et al. An improved swin transformer-based model for remote sensing object detection and instance segmentation[J]. Remote Sensing, 2021, 13 (23): 4779.
doi: 10.3390/rs13234779
|
15 |
LIU Z, LIN Y T, CAO Y, et al. Swin transformer: hierarchical vision transformer using shifted windows[C]//Proc. of the IEEE/CVF International Conference on Computer Vision, 2021: 9992-10002.
|
16 |
LI A , SUN S J , ZHANG Z Y , et al. A multi scale traffic object detection algorithm for road scenes based on improved YOLOv5[J]. Electronics, 2023, 12 (4): 878.
doi: 10.3390/electronics12040878
|
17 |
WANG J Q, CHEN K, XU R, et al. CARAFE: content-aware reassembly of features[C]//Proc. of the IEEE/CVF International Conference on Computer Vision, 2019: 3007-3016.
|
18 |
SHEN L Z , TAO H F , NI Y Z , et al. Improved YOLOv3 model with feature map cropping for multi-scale road object detection[J]. Measurement Science and Technology, 2023, 34 (4): 045406.
doi: 10.1088/1361-6501/acb075
|
19 |
LIU J , CAI Q Q , ZOU F M , et al. BiGA-YOLO: a lightweight object detection network based on YOLOv5 for autonomous driving[J]. Electronics, 2023, 12 (12): 2745.
doi: 10.3390/electronics12122745
|
20 |
TAN M X, PANG R M, LE Q V. EfficientDet: scalable and efficient object detection[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 10781-10790.
|
21 |
ZHANG Y , GUO Z Y , WU J Q , et al. Real-time vehicle detection based on improved YOLO v5[J]. Sustainability, 2022, 14 (19): 12274.
doi: 10.3390/su141912274
|
22 |
赵睿, 刘辉, 刘沛霖, 等. 基于改进YOLOv5s的安全帽检测算法[J]. 北京航空航天大学学报, 2023, 49 (8): 2050- 2061.
|
|
ZHAO R , LIU H , LIU P L , et al. Safety hat detection algorithm based on improved YOLOv5s[J]. Journal of Beijing University of Aeronautics and Astronautics, 2023, 49 (8): 2050- 2061.
|
23 |
LIANG Q K , XIANG S , HU Y C , et al. PD2SE-Net: computer-assisted plant disease diagnosis and severity estimation net-work[J]. Computers and Electronics in Agriculture, 2019, 157, 518- 529.
doi: 10.1016/j.compag.2019.01.034
|
24 |
ZHU X Z, HU H, LIN S, et al. Deformable convnets v2: more deformable, better results[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 9308-9316.
|
25 |
LUO Z X, SHEN T W, ZHOU L, et al. ContextDesc: local descriptor augmentation with cross-modality context[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 2527-2536.
|
26 |
GEIGER A , LENZ P , STILLER C , et al. Vision meets robotics: the KITTI dataset[J]. The International Journal of Robotics Research, 2013, 32 (11): 1231- 1237.
doi: 10.1177/0278364913491297
|
27 |
ZHANG S F, CHI C, YAO Y Q, et al. Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 9759-9768.
|
28 |
LIN T Y, DOLLAR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2117-2125.
|
29 |
LIU S, QI L, QIN H F, et al. Path aggregation network for instance segmentation[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 8759-8768.
|
30 |
RAO Y M , ZHAO W L , TANG Y S , et al. HorNet: efficient high-order spatial interactions with recursive gated convolutions[J]. Advances in Neural Information Processing Systems, 2022, 35, 10353- 10366.
|
31 |
LIU Z, MAO H Z, WU C Y, et al. A ConvNet for the 2020s[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 11976-11986.
|