Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (7): 2233-2242.DOI: 10.11772/j.issn.1001-9081.2023070918
• Multimedia computing and computer simulation • Previous Articles Next Articles
Yangyi GAO1,2, Tao LEI1,2(), Xiaogang DU1, Suiyong LI3, Yingbo WANG1, Chongdan MIN1,2
Received:
2023-07-11
Revised:
2023-09-18
Accepted:
2023-09-20
Online:
2023-10-26
Published:
2024-07-10
Contact:
Tao LEI
About author:
GAO Yangyi, born in 1998, M. S. candidate. His research interests include image processing, machine learning.Supported by:
高阳峄1,2, 雷涛1,2(), 杜晓刚1, 李岁永3, 王营博1, 闵重丹1,2
通讯作者:
雷涛
作者简介:
高阳峄(1998—),男,陕西咸阳人,硕士研究生,主要研究方向:图像处理、机器学习;基金资助:
CLC Number:
Yangyi GAO, Tao LEI, Xiaogang DU, Suiyong LI, Yingbo WANG, Chongdan MIN. Crowd counting and locating method based on pixel distance map and four-dimensional dynamic convolutional network[J]. Journal of Computer Applications, 2024, 44(7): 2233-2242.
高阳峄, 雷涛, 杜晓刚, 李岁永, 王营博, 闵重丹. 基于像素距离图和四维动态卷积网络的密集人群计数与定位方法[J]. 《计算机应用》唯一官方网站, 2024, 44(7): 2233-2242.
Add to citation manager EndNote|Ris|BibTeX
URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023070918
分辨率 | 阶段1 | 阶段2 | 阶段3 | 阶段4 |
---|---|---|---|---|
1/4 | ![]() | ![]() | ![]() | ![]() |
1/8 | ![]() | ![]() | ![]() | |
1/16 | ![]() | ![]() | ||
1/32 | ![]() |
Tab. 1 High-resolution network structure parameters
分辨率 | 阶段1 | 阶段2 | 阶段3 | 阶段4 |
---|---|---|---|---|
1/4 | ![]() | ![]() | ![]() | ![]() |
1/8 | ![]() | ![]() | ![]() | |
1/16 | ![]() | ![]() | ||
1/32 | ![]() |
方法 | Shanghai Tech Part A | Shanghai Tech Part B | UCF-QRNF | |||
---|---|---|---|---|---|---|
MAE | MSE | MAE | MSE | MAE | MSE | |
MCNN[ | 110.2 | 173.2 | 26.4 | 41.3 | 277.0 | 426.0 |
CSRNet[ | 68.2 | 115.0 | 10.6 | 16.0 | 121.3 | 208.0 |
DADNet[ | 64.2 | 99.9 | 8.8 | 13.5 | 113.2 | 189.4 |
SCAR[ | 66.3 | 114.1 | 9.5 | 15.2 | 132.6 | 177.4 |
SFCN+[ | 64.8 | 107.5 | 7.6 | 13.0 | 114.5 | 193.6 |
SUA-Fully[ | 66.9 | 125.6 | 12.3 | 17.9 | 119.2 | 213.3 |
MFP-Net[ | 65.5 | 112.5 | 8.7 | 13.8 | 112.0 | 190.7 |
NDConv[ | 61.4 | 104.2 | 7.8 | 13.8 | 91.2 | 165.6 |
SC2Net[ | 58.9 | 97.7 | 6.9 | 11.4 | 98.5 | 174.5 |
TransCrowd[ | 66.1 | 105.1 | 9.3 | 16.1 | 99.1 | 168.5 |
DLMP-Net[ | 59.2 | 90.7 | 7.1 | 11.3 | 87.7 | 169.7 |
DMCNet[ | 58.5 | 84.5 | 8.6 | 13.7 | 96.5 | 164.0 |
CHS-Net[ | 59.2 | 97.8 | 7.1 | 11.2 | 83.4 | 144.9 |
本文方法 | 57.5 | 104.4 | 6.8 | 11.8 | 88.4 | 153.2 |
Tab. 2 Comparison of counting performance on ShanghaiTech and UCF-QRNF datasets
方法 | Shanghai Tech Part A | Shanghai Tech Part B | UCF-QRNF | |||
---|---|---|---|---|---|---|
MAE | MSE | MAE | MSE | MAE | MSE | |
MCNN[ | 110.2 | 173.2 | 26.4 | 41.3 | 277.0 | 426.0 |
CSRNet[ | 68.2 | 115.0 | 10.6 | 16.0 | 121.3 | 208.0 |
DADNet[ | 64.2 | 99.9 | 8.8 | 13.5 | 113.2 | 189.4 |
SCAR[ | 66.3 | 114.1 | 9.5 | 15.2 | 132.6 | 177.4 |
SFCN+[ | 64.8 | 107.5 | 7.6 | 13.0 | 114.5 | 193.6 |
SUA-Fully[ | 66.9 | 125.6 | 12.3 | 17.9 | 119.2 | 213.3 |
MFP-Net[ | 65.5 | 112.5 | 8.7 | 13.8 | 112.0 | 190.7 |
NDConv[ | 61.4 | 104.2 | 7.8 | 13.8 | 91.2 | 165.6 |
SC2Net[ | 58.9 | 97.7 | 6.9 | 11.4 | 98.5 | 174.5 |
TransCrowd[ | 66.1 | 105.1 | 9.3 | 16.1 | 99.1 | 168.5 |
DLMP-Net[ | 59.2 | 90.7 | 7.1 | 11.3 | 87.7 | 169.7 |
DMCNet[ | 58.5 | 84.5 | 8.6 | 13.7 | 96.5 | 164.0 |
CHS-Net[ | 59.2 | 97.8 | 7.1 | 11.2 | 83.4 | 144.9 |
本文方法 | 57.5 | 104.4 | 6.8 | 11.8 | 88.4 | 153.2 |
方法 | Val | Test | ||
---|---|---|---|---|
MAE | MSE | MAE | MSE | |
MCNN[ | 218.5 | 700.6 | 232.5 | 714.6 |
CSRNet[ | 104.8 | 433.8 | 190.6 | 491.4 |
SCAR[ | 81.6 | 397.9 | 110.0 | 495.3 |
SFCN+[ | 95.5 | 608.3 | 105.7 | 424.1 |
DM-Count[ | — | — | 88.4 | 388.6 |
SUA-Fully[ | 81.8 | 439.1 | 105.8 | 445.3 |
SC2Net[ | — | — | 89.7 | 348.9 |
MFP-Net[ | 84.2 | 434.4 | 90.3 | 458.0 |
TransCrowd[ | 88.4 | 400.5 | 117.7 | 451.0 |
DLMP-Net[ | 72.4 | 383.3 | 87.7 | 431.6 |
MAN[ | — | — | 76.5 | 323.3 |
CU-Count[ | — | — | 108.7 | 458.0 |
本文方法 | 62.7 | 259.1 | 82.4 | 334.7 |
Tab. 3 Comparison counting performance on NWPU-Crowd datasets
方法 | Val | Test | ||
---|---|---|---|---|
MAE | MSE | MAE | MSE | |
MCNN[ | 218.5 | 700.6 | 232.5 | 714.6 |
CSRNet[ | 104.8 | 433.8 | 190.6 | 491.4 |
SCAR[ | 81.6 | 397.9 | 110.0 | 495.3 |
SFCN+[ | 95.5 | 608.3 | 105.7 | 424.1 |
DM-Count[ | — | — | 88.4 | 388.6 |
SUA-Fully[ | 81.8 | 439.1 | 105.8 | 445.3 |
SC2Net[ | — | — | 89.7 | 348.9 |
MFP-Net[ | 84.2 | 434.4 | 90.3 | 458.0 |
TransCrowd[ | 88.4 | 400.5 | 117.7 | 451.0 |
DLMP-Net[ | 72.4 | 383.3 | 87.7 | 431.6 |
MAN[ | — | — | 76.5 | 323.3 |
CU-Count[ | — | — | 108.7 | 458.0 |
本文方法 | 62.7 | 259.1 | 82.4 | 334.7 |
方法 | Shanghai Tech Part A | Shanghai Tech Part B | UCF-QRNF | ||||||
---|---|---|---|---|---|---|---|---|---|
F1 | 精确率 | 召回率 | F1 | 精确率 | 召回率 | F1 | 精确率 | 召回率 | |
TFaces[ | 57.3 | 43.1 | 85.5 | 71.1 | 64.7 | 79.0 | 49.4 | 36.3 | 77.3 |
RALoc[ | 69.2 | 61.3 | 79.5 | 68.0 | 60.0 | 78.3 | 53.3 | 59.4 | 48.3 |
LSC[ | 68.0 | 69.6 | 66.5 | 71.2 | 71.7 | 70.6 | 58.2 | 58.6 | 57.7 |
GL[ | — | — | — | — | — | — | 78.2 | 74.8 | 76.4 |
CLTR[ | — | — | — | — | — | — | 82.2 | 79.7 | 80.9 |
TopoCount[ | 74.6 | 72.7 | 73.6 | 75.3 | 74.6 | 73.7 | 81.8 | 79.0 | 80.3 |
本文方法 | 77.3 | 77.0 | 77.6 | 84.2 | 81.7 | 82.1 | 82.3 | 81.1 | 83.5 |
Tab. 4 Comparison localing performance on ShanghaiTech and UCF-QRNF datasets
方法 | Shanghai Tech Part A | Shanghai Tech Part B | UCF-QRNF | ||||||
---|---|---|---|---|---|---|---|---|---|
F1 | 精确率 | 召回率 | F1 | 精确率 | 召回率 | F1 | 精确率 | 召回率 | |
TFaces[ | 57.3 | 43.1 | 85.5 | 71.1 | 64.7 | 79.0 | 49.4 | 36.3 | 77.3 |
RALoc[ | 69.2 | 61.3 | 79.5 | 68.0 | 60.0 | 78.3 | 53.3 | 59.4 | 48.3 |
LSC[ | 68.0 | 69.6 | 66.5 | 71.2 | 71.7 | 70.6 | 58.2 | 58.6 | 57.7 |
GL[ | — | — | — | — | — | — | 78.2 | 74.8 | 76.4 |
CLTR[ | — | — | — | — | — | — | 82.2 | 79.7 | 80.9 |
TopoCount[ | 74.6 | 72.7 | 73.6 | 75.3 | 74.6 | 73.7 | 81.8 | 79.0 | 80.3 |
本文方法 | 77.3 | 77.0 | 77.6 | 84.2 | 81.7 | 82.1 | 82.3 | 81.1 | 83.5 |
方法 | Val | Test | ||||
---|---|---|---|---|---|---|
F1 | 精确率 | 召回率 | F1 | 精确率 | 召回率 | |
Faster-RCNN[ | 7.3 | 96.4 | 3.8 | 6.7 | 95.8 | 3.5 |
TFaces[ | 59.8 | 54.3 | 66.6 | 71.1 | 64.7 | 79.0 |
RALoc[ | 62.5 | 69.2 | 56.9 | 68.0 | 60.0 | 78.3 |
GL[ | — | — | — | 80.0 | 56.2 | 66.0 |
CLTR[ | 73.9 | 71.3 | 72.6 | 69.4 | 67.6 | 68.5 |
TopoCount[ | — | — | — | 69.1 | 69.5 | 68.7 |
本文方法 | 74.9 | 78.4 | 70.1 | 71.2 | 73.6 | 68.4 |
Tab. 5 Comparison localing performance on NWPU-Crowd dataset
方法 | Val | Test | ||||
---|---|---|---|---|---|---|
F1 | 精确率 | 召回率 | F1 | 精确率 | 召回率 | |
Faster-RCNN[ | 7.3 | 96.4 | 3.8 | 6.7 | 95.8 | 3.5 |
TFaces[ | 59.8 | 54.3 | 66.6 | 71.1 | 64.7 | 79.0 |
RALoc[ | 62.5 | 69.2 | 56.9 | 68.0 | 60.0 | 78.3 |
GL[ | — | — | — | 80.0 | 56.2 | 66.0 |
CLTR[ | 73.9 | 71.3 | 72.6 | 69.4 | 67.6 | 68.5 |
TopoCount[ | — | — | — | 69.1 | 69.5 | 68.7 |
本文方法 | 74.9 | 78.4 | 70.1 | 71.2 | 73.6 | 68.4 |
方法 | 参数量/106 | 计算量/GFLOPs |
---|---|---|
CSRNet[ | 16.2 | 857.8 |
TransCrowd[ | 86.8 | 49.3 |
本文方法 | 66.5 | 35.4 |
Tab. 6 Comparison of parameter and computational quautity of different methods
方法 | 参数量/106 | 计算量/GFLOPs |
---|---|---|
CSRNet[ | 16.2 | 857.8 |
TransCrowd[ | 86.8 | 49.3 |
本文方法 | 66.5 | 35.4 |
方法 | 计数 | 定位 | |
---|---|---|---|
MAE | MSE | F1/% | |
高分辨率网络+Gaussian-Map | 59.6 | 108.1 | 71.1 |
高分辨率网络+PDMap | 57.5 | 103.4 | 77.0 |
Tab. 7 Ablation experiment results of pixel distance map
方法 | 计数 | 定位 | |
---|---|---|---|
MAE | MSE | F1/% | |
高分辨率网络+Gaussian-Map | 59.6 | 108.1 | 71.1 |
高分辨率网络+PDMap | 57.5 | 103.4 | 77.0 |
方法 | MAE | MSE |
---|---|---|
CSRNet[ | 66.4 | 108.0 |
DLMP-Net[ | 58.6 | 85.2 |
高分辨率网络 [ | 58.1 | 80.3 |
Tab. 8 Ablation experiment results of high-resolution network
方法 | MAE | MSE |
---|---|---|
CSRNet[ | 66.4 | 108.0 |
DLMP-Net[ | 58.6 | 85.2 |
高分辨率网络 [ | 58.1 | 80.3 |
方法 | MAE | MSE |
---|---|---|
高分辨率网络-PDMap | 66.5 | 115.1 |
+CondConv[ | 62.7 | 107.6 |
+DyConv[ | 61.1 | 107.1 |
+FDDC | 57.5 | 103.4 |
Tab. 9 Ablation experiment results of four-dimensional dynamic convolution
方法 | MAE | MSE |
---|---|---|
高分辨率网络-PDMap | 66.5 | 115.1 |
+CondConv[ | 62.7 | 107.6 |
+DyConv[ | 61.1 | 107.1 |
+FDDC | 57.5 | 103.4 |
1 | FAN Z, ZhANG H, ZHANG Z, et al. A survey of crowd counting and density estimation based on convolutional neural network [J]. Neurocomputing, 2022, 472: 224-251. |
2 | LEI Y, LIU Y, ZHANG P, et al. Towards using count-level weak supervision for crowd counting [J]. Pattern Recognition, 2021, 109: 107616. |
3 | LI H, LIU L, YANG K, et al. Video crowd localization with multifocus Gaussian neighborhood attention and a large-scale benchmark [J]. IEEE Transactions on Image Processing, 2022, 31: 6032-6047. |
4 | YANG S, GUO W, REN Y. CrowdFormer: an overlap patching vision transformer for top-down crowd counting [C]// Proceedings of the 31st International Joint Conference on Artificial Intelligence. California: ijcai.org, 2022: 1545-1551. |
5 | ZHANG Y, CHOI S, HONG S. Spatio-channel attention blocks for cross-modal crowd counting [C]// Proceedings of the 16th Asian Conference on Computer Vision. Cham: Springer, 2022: 22-40. |
6 | ZHONG X, YAN Z, QIN J, et al. An improved normed-deformable convolution for crowd counting [J]. IEEE Signal Processing Letters, 2022, 29: 1794-1798. |
7 | KHAN M A, MENOUAR H, HAMILA R. Revisiting crowd counting: state-of-the-art, trends, and future perspectives [J]. Image and Vision Computing, 2023, 129: 104597. |
8 | CHEN X, BIN Y, GAO C, et al. Relevant region prediction for crowd counting [J]. Neurocomputing, 2020, 407: 399-408. |
9 | ZHANG Y, ZHOU D, CHEN S, et al. Single-image crowd counting via multi-column convolutional neural network [C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 589-597. |
10 | LI Y, ZHANG X, CHEN D. CSRNet: dilated convolutional neural networks for understanding the highly congested scenes [C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 1091-1100. |
11 | GUO D, LI K, ZHA Z-J, et al. DADNet: dilated-attention-deformable ConvNet for crowd counting [C]// Proceedings of the 27th ACM International Conference on Multimedia. New York: ACM, 2019: 1823-1832. |
12 | GAO J, WANG Q, YUAN Y. SCAR: spatial-/channel-wise attention regression networks for crowd counting [J]. Neurocomputing, 2019, 363: 1-8. |
13 | LIANG D, CHEN X, XU W, et al. TransCrowd: weakly-supervised crowd counting with transformers [J]. SCIENCE CHINA Information Sciences, 2022, 65: 1600104. |
14 | WANG Q, GAO J, LIN W, et al. Pixel-wise crowd understanding via synthetic data [J]. International Journal of Computer Vision, 2021, 129: 225-245. |
15 | WANG Q, GAO J, LIN W, et al. NWPU-Crowd: a large-scale benchmark for crowd counting and localization [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 43(6): 2141-2149. |
16 | CHEN Q, LEI T, GENG X, et al. DLMP-Net: a dynamic yet lightweight multi-pyramid network for crowd density estimation [C]// Proceedings of the 5th Chinese Conference on Pattern Recognition and Computer Vision. Cham: Springer, 2022: 27-39. |
17 | IDREES H, TAYYAB M, ATHREY K, et al. Composition loss for counting, density map estimation and localization in dense crowds [C]// Proceedings of the 15th European Conference on Computer Vision. Cham: Springer, 2018: 544-559. |
18 | ABOUSAMRA S, HOAI M, SAMARAS D, et al. Localization in the crowd with topological constraints [J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(2): 872-881. |
19 | LIU W, SALZMANN M, FUA P. Context-aware crowd counting [C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 5094-5103. |
20 | ZAND M, DAMIRCHI H, FARLEY A, et al. Multiscale crowd counting and localization by multitask point supervision [C]// Proceedings of the 2022 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2022: 1820-1824. |
21 | SONG Q, WANG C, JIANG Z, et al. Rethinking counting and localization in crowds: a purely point-based framework [C]// Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2021: 3345-3354. |
22 | LARADJI I H, ROSTAMZADEH N, PINHEIRO P O, et al. Where are the blobs: counting by localization with point supervision [C]// Proceedings of the 15th European Conference on Computer Vision. Cham: Springer, 2018: 547-562. |
23 | LIU Y, SHI M, ZHAO Q, et al. Point in, box out: beyond counting persons in crowds [C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 6462-6471. |
24 | GAO J, GONG M, LI X. Congested crowd instance localization with dilated convolutional Swin Transformer [J]. Neurocomputing, 2022, 513: 94-103. |
25 | SAM D B, PERI S V, SUNDARARAMAN M N, et al. Locate, size, and count: accurately resolving people in dense crowds via detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(8): 2739-2751. |
26 | YANG B, BENDER G, LE Q V, et al. CondConv: conditionally parameterized convolutions for efficient inference [C]// Proceedings of the 33rd International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2019: 1307-1318. |
27 | CHEN Y, DAI X, LIU M, et al. Dynamic convolution: attention over convolution kernels [C]// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 11027-11036. |
28 | ZHANG Y, ZHANG J, WANG Q, et al. DyNet: dynamic convolution for accelerating convolutional neural networks [EB/OL]. [2023-07-01]. . |
29 | OLMSCHENK G, TANG H, ZHU Z. Improving dense crowd counting convolutional neural networks using inverse k-nearest neighbor maps and multiscale upsampling [EB/OL]. [2023-07-01]. . |
30 | SUN K, XIAO B, LIU D, et al. Deep high-resolution representation learning for human pose estimation [C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 5686-5796. |
31 | HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition [C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 770-778. |
32 | DAI M, HUANG Z, GAO J, et al. Cross-head supervision for crowd counting with noisy annotations [C]// Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2023: 1-5. |
33 | WANG M, CAI H, DAI Y, et al. Dynamic mixture of counter network for location-agnostic crowd counting [C]// Proceedings of the 2023 IEEE/CVF Winter Conference on Applications of Computer Vision. Piscataway: IEEE, 2023: 167-177. |
34 | LIN H, MA Z, JI R, et al. Boosting crowd counting via multifaceted attention [C]// Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2022: 19596-19605. |
35 | LI C, HU X, ABOUSAMRA S, et al. Calibrating uncertainty for semi-supervised crowd counting [EB/OL]. [2023-07-01]. . |
36 | MENG Y, ZHANG H, ZHAO Y, et al. Spatial uncertainty-aware semi-supervised crowd counting [C]// Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2021: 15529-15539. |
37 | LEI T, ZHANG D, WANG R, et al. MFP-Net: multi-scale feature pyramid network for crowd counting [J]. IET Image Processing, 2021, 15(14): 3522-3533. |
38 | LIANG L, ZHAO H, ZHOU F, et al. SC2Net: scale-aware crowd counting network with pyramid dilated convolution [J]. Applied Intelligence, 2023, 53: 5146-5159. |
39 | WANG B, LIU H, SAMARAS D, et al. Distribution matching for crowd counting [C]// Proceedings of the 34th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2020: 1595-1607. |
40 | HU P, RAMANAN D. Finding tiny faces [C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 1522-1530. |
41 | LIANG D, XU W, BAI X. An end-to-end transformer model for crowd localization [C]// Proceedings of the 17th European Conference on Computer Vision. Cham: Springer, 2022: 38-54. |
42 | LIU C, WENG X, MU Y. Recurrent attentive zooming for joint crowd counting and precise localization [C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 1217-1226. |
43 | REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks [C]// Proceedings of the 28th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2015: 91-99. |
44 | WAN J, LIU Z, CHAN A B. A generalized loss function for crowd counting and localization [C]// Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 1974-1983. |
[1] | Yun LI, Fuyou WANG, Peiguang JING, Su WANG, Ao XIAO. Uncertainty-based frame associated short video event detection method [J]. Journal of Computer Applications, 2024, 44(9): 2903-2910. |
[2] | Zhiqiang ZHAO, Peihong MA, Xinhong HEI. Crowd counting method based on dual attention mechanism [J]. Journal of Computer Applications, 2024, 44(9): 2886-2892. |
[3] | Hong CHEN, Bing QI, Haibo JIN, Cong WU, Li’ang ZHANG. Class-imbalanced traffic abnormal detection based on 1D-CNN and BiGRU [J]. Journal of Computer Applications, 2024, 44(8): 2493-2499. |
[4] | Xun SUN, Ruifeng FENG, Yanru CHEN. Monocular 3D object detection method integrating depth and instance segmentation [J]. Journal of Computer Applications, 2024, 44(7): 2208-2215. |
[5] | Dongwei WANG, Baichen LIU, Zhi HAN, Yanmei WANG, Yandong TANG. Deep network compression method based on low-rank decomposition and vector quantization [J]. Journal of Computer Applications, 2024, 44(7): 1987-1994. |
[6] | Wei LI, Xiaorong ZHANG, Peng CHEN, Qing LI, Changqing ZHANG. Crowd counting algorithm with multi-scale fusion based on normal inverse Gamma distribution [J]. Journal of Computer Applications, 2024, 44(7): 2243-2249. |
[7] | Mengyuan HUANG, Kan CHANG, Mingyang LING, Xinjie WEI, Tuanfa QIN. Progressive enhancement algorithm for low-light images based on layer guidance [J]. Journal of Computer Applications, 2024, 44(6): 1911-1919. |
[8] | Jianjing LI, Guanfeng LI, Feizhou QIN, Weijun LI. Multi-relation approximate reasoning model based on uncertain knowledge graph embedding [J]. Journal of Computer Applications, 2024, 44(6): 1751-1759. |
[9] | Wenshuo GAO, Xiaoyun CHEN. Point cloud classification network based on node structure [J]. Journal of Computer Applications, 2024, 44(5): 1471-1478. |
[10] | Min SUN, Qian CHENG, Xining DING. CBAM-CGRU-SVM based malware detection method for Android [J]. Journal of Computer Applications, 2024, 44(5): 1539-1545. |
[11] | Tianhua CHEN, Jiaxuan ZHU, Jie YIN. Bird recognition algorithm based on attention mechanism [J]. Journal of Computer Applications, 2024, 44(4): 1114-1120. |
[12] | Lijun XU, Hui LI, Zuyang LIU, Kansong CHEN, Weixuan MA. 3D-GA-Unet: MRI image segmentation algorithm for glioma based on 3D-Ghost CNN [J]. Journal of Computer Applications, 2024, 44(4): 1294-1302. |
[13] | Jie WANG, Hua MENG. Image classification algorithm based on overall topological structure of point cloud [J]. Journal of Computer Applications, 2024, 44(4): 1107-1113. |
[14] | Yongfeng DONG, Jiaming BAI, Liqin WANG, Xu WANG. Chinese named entity recognition combining prior knowledge and glyph features [J]. Journal of Computer Applications, 2024, 44(3): 702-708. |
[15] | Ruifeng HOU, Pengcheng ZHANG, Liyuan ZHANG, Zhiguo GUI, Yi LIU, Haowen ZHANG, Shubin WANG. Iterative denoising network based on total variation regular term expansion [J]. Journal of Computer Applications, 2024, 44(3): 916-921. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||