Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (7): 2147-2154.DOI: 10.11772/j.issn.1001-9081.2022060823
Special Issue: 人工智能
• Artificial intelligence • Previous Articles Next Articles
Zhangjian JI1,2(), Ming ZHANG1,2, Zilong WANG1,2
Received:
2022-06-08
Revised:
2022-08-30
Accepted:
2022-09-02
Online:
2022-09-23
Published:
2023-07-10
Contact:
Zhangjian JI
About author:
JI Zhangjian, born in 1983, Ph. D., associate professor. His research interests include computer vision, machine learning.Supported by:
通讯作者:
姬张建
作者简介:
姬张建(1983—),男,陕西澄城人,副教授,博士,CCF会员,主要研究方向:计算机视觉、机器学习;基金资助:
CLC Number:
Zhangjian JI, Ming ZHANG, Zilong WANG. High-precision object detection algorithm based on improved VarifocalNet[J]. Journal of Computer Applications, 2023, 43(7): 2147-2154.
姬张建, 张明, 王子龙. 基于改进VarifocalNet的高精度目标检测算法[J]. 《计算机应用》唯一官方网站, 2023, 43(7): 2147-2154.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022060823
算法 | 主干网 | 迭代次数 | mAP/% | AP50/% | AP75/% | APS/% | APM/% | APL/% |
---|---|---|---|---|---|---|---|---|
ATSS | ResNet-101 | 24 | 43.6 | 62.1 | 47.4 | 26.1 | 47.0 | 53.6 |
PAA | ResNet-101 | 24 | 44.8 | 63.3 | 48.7 | 26.5 | 48.8 | 56.3 |
GFLV1 | ResNet-101 | 24 | 45.0 | 63.7 | 48.9 | 27.2 | 48.8 | 54.5 |
IQDet | ResNet-101 | 12 | 45.1 | 63.4 | 49.3 | 26.7 | 48.5 | 56.6 |
YOLOF | ResNet-101 | 12 | 42.2 | 62.1 | 45.7 | 23.2 | 47.0 | 57.7 |
VFNet | ResNet-101 | 24 | 46.7 | 64.9 | 50.8 | 28.4 | 50.2 | 57.6 |
VFNet | ResNet-101 | 12 | 43.6 | 61.8 | 47.3 | 24.9 | 46.8 | 54.4 |
本文算法1_1x | RLANet-101 | 12 | 45.3 | 63.6 | 49.1 | 26.6 | 48.5 | 56.3 |
本文算法1_2x | RLANet-101 | 24 | 47.8 | 66.1 | 51.9 | 29.5 | 51.3 | 58.8 |
DETR | ResNet-50 | 300 | 42.1 | 63.1 | 44.5 | 19.7 | 46.3 | 60.9 |
UP-DETR | ResNet-50 | 300 | 42.8 | 63.0 | 45.3 | 20.8 | 47.1 | 61.7 |
DyHead | ResNet-50 | 12 | 43.0 | 60.7 | 46.8 | 24.7 | 46.4 | 53.9 |
GFLV1 | ResNet-50 | 24 | 43.1 | 62.0 | 46.8 | 26.0 | 46.7 | 52.3 |
VFNet | ResNet-50 | 12 | 41.6 | 59.8 | 45.0 | 23.9 | 44.6 | 51.4 |
VFNet | ResNet-50 | 24 | 44.8 | 63.1 | 48.7 | 27.2 | 48.1 | 54.8 |
本文算法2_1x | RLANet-50 | 12 | 43.1 | 61.1 | 46.7 | 25.2 | 46.0 | 53.1 |
本文算法2_2x | RLANet-50 | 24 | 45.4 | 63.4 | 49.3 | 27.7 | 48.3 | 55.7 |
本文算法3_1x | RLANet-50 | 12 | 45.9 | 63.9 | 49.8 | 26.2 | 49.6 | 57.6 |
Tab. 1 Performance comparison of the proposed algorithms with other advanced algorithms on dataset COCO test-dev 2017
算法 | 主干网 | 迭代次数 | mAP/% | AP50/% | AP75/% | APS/% | APM/% | APL/% |
---|---|---|---|---|---|---|---|---|
ATSS | ResNet-101 | 24 | 43.6 | 62.1 | 47.4 | 26.1 | 47.0 | 53.6 |
PAA | ResNet-101 | 24 | 44.8 | 63.3 | 48.7 | 26.5 | 48.8 | 56.3 |
GFLV1 | ResNet-101 | 24 | 45.0 | 63.7 | 48.9 | 27.2 | 48.8 | 54.5 |
IQDet | ResNet-101 | 12 | 45.1 | 63.4 | 49.3 | 26.7 | 48.5 | 56.6 |
YOLOF | ResNet-101 | 12 | 42.2 | 62.1 | 45.7 | 23.2 | 47.0 | 57.7 |
VFNet | ResNet-101 | 24 | 46.7 | 64.9 | 50.8 | 28.4 | 50.2 | 57.6 |
VFNet | ResNet-101 | 12 | 43.6 | 61.8 | 47.3 | 24.9 | 46.8 | 54.4 |
本文算法1_1x | RLANet-101 | 12 | 45.3 | 63.6 | 49.1 | 26.6 | 48.5 | 56.3 |
本文算法1_2x | RLANet-101 | 24 | 47.8 | 66.1 | 51.9 | 29.5 | 51.3 | 58.8 |
DETR | ResNet-50 | 300 | 42.1 | 63.1 | 44.5 | 19.7 | 46.3 | 60.9 |
UP-DETR | ResNet-50 | 300 | 42.8 | 63.0 | 45.3 | 20.8 | 47.1 | 61.7 |
DyHead | ResNet-50 | 12 | 43.0 | 60.7 | 46.8 | 24.7 | 46.4 | 53.9 |
GFLV1 | ResNet-50 | 24 | 43.1 | 62.0 | 46.8 | 26.0 | 46.7 | 52.3 |
VFNet | ResNet-50 | 12 | 41.6 | 59.8 | 45.0 | 23.9 | 44.6 | 51.4 |
VFNet | ResNet-50 | 24 | 44.8 | 63.1 | 48.7 | 27.2 | 48.1 | 54.8 |
本文算法2_1x | RLANet-50 | 12 | 43.1 | 61.1 | 46.7 | 25.2 | 46.0 | 53.1 |
本文算法2_2x | RLANet-50 | 24 | 45.4 | 63.4 | 49.3 | 27.7 | 48.3 | 55.7 |
本文算法3_1x | RLANet-50 | 12 | 45.9 | 63.9 | 49.8 | 26.2 | 49.6 | 57.6 |
主干网 | 特征金字塔 | mAP | AP50 | AP75 | APS | APM | APL |
---|---|---|---|---|---|---|---|
ResNet-50 | FPN | 41.6 | 59.8 | 45.0 | 23.9 | 44.6 | 51.4 |
ResNet-50 | FPN_DCN | 41.8(+0.2) | 59.9 | 45.4 | 24.7 | 44.8 | 50.8 |
RLANet-50 | FPN | 42.9(+1.3) | 61.0 | 46.5 | 24.7 | 45.8 | 53.6 |
RLANet-50 | FPN_DCN | 43.1(+1.5) | 61.1 | 46.7 | 25.2 | 46.0 | 53.1 |
Tab. 2 Ablation experimental results of detector components on dataset COCO test-dev 2017
主干网 | 特征金字塔 | mAP | AP50 | AP75 | APS | APM | APL |
---|---|---|---|---|---|---|---|
ResNet-50 | FPN | 41.6 | 59.8 | 45.0 | 23.9 | 44.6 | 51.4 |
ResNet-50 | FPN_DCN | 41.8(+0.2) | 59.9 | 45.4 | 24.7 | 44.8 | 50.8 |
RLANet-50 | FPN | 42.9(+1.3) | 61.0 | 46.5 | 24.7 | 45.8 | 53.6 |
RLANet-50 | FPN_DCN | 43.1(+1.5) | 61.1 | 46.7 | 25.2 | 46.0 | 53.1 |
算法 | 主干网 | 特征金字塔 | 浮点计算量/GFLPOs | 参数量/106 |
---|---|---|---|---|
VFNet | ResNet-50 | FPN | 192.78 | 32.67 |
本文算法 | RLANet-50 | FPN_DCN | 212.95 | 36.67 |
Tab. 3 Comparison of the number of parameters and computational cost of VFNet and the proposed algorithm
算法 | 主干网 | 特征金字塔 | 浮点计算量/GFLPOs | 参数量/106 |
---|---|---|---|---|
VFNet | ResNet-50 | FPN | 192.78 | 32.67 |
本文算法 | RLANet-50 | FPN_DCN | 212.95 | 36.67 |
算法 | mAP | AP50 | AP75 | APS | APM | APL |
---|---|---|---|---|---|---|
Student_RLANet-50 | 43.1 | 61.1 | 46.7 | 25.2 | 46.0 | 53.1 |
Teacher_RLANet-101 | 45.3 | 63.6 | 49.1 | 26.6 | 48.5 | 56.3 |
RLANet-101_FGD_RLANet-50 | 45.9 (+2.8) | 63.9 (+2.8) | 49.8 (+3.1) | 26.2 (+1.0) | 49.6 (+3.6) | 57.6 (+4.5) |
Tab. 4 Distillation experiments on RLANet-50 using RLANet-101
算法 | mAP | AP50 | AP75 | APS | APM | APL |
---|---|---|---|---|---|---|
Student_RLANet-50 | 43.1 | 61.1 | 46.7 | 25.2 | 46.0 | 53.1 |
Teacher_RLANet-101 | 45.3 | 63.6 | 49.1 | 26.6 | 48.5 | 56.3 |
RLANet-101_FGD_RLANet-50 | 45.9 (+2.8) | 63.9 (+2.8) | 49.8 (+3.1) | 26.2 (+1.0) | 49.6 (+3.6) | 57.6 (+4.5) |
算法 | 主干网 | 特征金字塔网络 | 浮点计算量/GFLOPs | 参数量/106 |
---|---|---|---|---|
Student_RLANet-50 | RLANet-50 | FPN_DCN | 192.78 | 32.67 |
Teacher_RLANet-101 | RLANet-101 | FPN_DCN | 292.51 | 55.81 |
RLANet-101_FGD_RLANet-50 | RLANet-50 | FPN_DCN | 192.78 | 32.67 |
Tab. 5 Comparison of the number of parameters and computational cost before and after distillation operation
算法 | 主干网 | 特征金字塔网络 | 浮点计算量/GFLOPs | 参数量/106 |
---|---|---|---|---|
Student_RLANet-50 | RLANet-50 | FPN_DCN | 192.78 | 32.67 |
Teacher_RLANet-101 | RLANet-101 | FPN_DCN | 292.51 | 55.81 |
RLANet-101_FGD_RLANet-50 | RLANet-50 | FPN_DCN | 192.78 | 32.67 |
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