[1]CHENG Deqiang,MA Shang,KOU Qiqi,et al.Target detection algorithm for improving feature fusion and global perception based on YOLOv4[J].CAAI Transactions on Intelligent Systems,2024,19(2):325-334.[doi:10.11992/tis.202207018]
Copy

Target detection algorithm for improving feature fusion and global perception based on YOLOv4

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 2 Page number: 325-334 Column: 学术论文—机器感知与模式识别 Public date: 2024-03-05
Title:
Target detection algorithm for improving feature fusion and global perception based on YOLOv4
Author(s):
CHENG Deqiang1;  MA Shang1;  KOU Qiqi2;  ZHANG Haoxiang1;  QIAN Jiansheng1
1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China;
2. School of Computer Science & Technology, China University of Mining and Technology, Xuzhou 221116, China
Keywords:
YOLOv4; target detection; feature fusion; cross-scale; multiscale variation; global attention; average pooling; contextual information
CLC:
TP391
DOI:
10.11992/tis.202207018
Abstract:
The YOLOv4 algorithm has a good balance in detection speed and accuracy, but there are still drawbacks of inaccurate positioning frame and low detection rate, especially for small detection targets and great changes in scale. Dealing with these problems, a new YOLOv4-based target detection algorithm is developed. The algorithm utilizes an enhanced feature fusion module—PANet combined with the bidirectional feature pyramid network instead of PANet to increase cross-scale connections, introduce weights at the output to improve the expressiveness of important features and solve accuracy degradation as a result of multiscale changes. Afterward, a new global association network is adopted to improve the output of the Sigmoid function while reducing the average pooling and computation, strengthen the model’s learning of the contextual relationship of the target, and reduce noise interference and global information loss. The RSOD and NWPU VHR-10 datasets are employed here, with average detection accuracies being enhanced by about 5% and 3%, respectively; the generalization experiment uses the VOC2007 + 2012 public dataset, with the average detection accuracy being enhanced by about 0.6%. The experimental results reveal that the improved algorithm can effectively enhance the detection ability of the model.
References:
[1] 程德强, 李腾腾, 郭昕, 等. 改进的SIFT邻域投票图像匹配算法[J]. 计算机工程与设计, 2020, 41(1): 162–168
CHENG Deqiang, LI Tengteng, GUO Xin, et al. Improved SIFT neighborhood voting image matching algorithm[J]. Computer engineering and design, 2020, 41(1): 162–168
[2] CHENG Deqiang, TANG Shixuan, FENG Chenchen, et al. Extended HOG-CLBC for pedstrain detection[J]. Opto-electronic engineer, 2018, 45(8): 180111.
[3] 张桂梅, 张松, 储珺. 一种新的基于局部轮廓特征的目标检测方法[J]. 自动化学报, 2014, 40(10): 2346–2355
ZHANG Guimei, ZHANG Song, CHU Jun. A new object detection algorithm using local contour features[J]. Acta automatica sinica, 2014, 40(10): 2346–2355
[4] 王彦情, 马雷, 田原. 光学遥感图像舰船目标检测与识别综述[J]. 自动化学报, 2011, 37(9): 1029–1039
WANG Yanqing, MA Lei, TIAN Yuan. State-of-the-art of ship detection and recognition in optical remotely sensed imagery[J]. Acta automatica sinica, 2011, 37(9): 1029–1039
[5] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. New York: ACM, 2014: 580-587.
[6] CHE Xiangjiu, LIU Hualuo, SHAO Qingbin. Fabric defect recognition algorithm based on improved Fast RCNN[J]. Journal of Jilin University (Engineering and Technology Edition), 2019, 49(6): 2038–2044.
[7] 黄继鹏, 史颖欢, 高阳. 面向小目标的多尺度Faster-RCNN检测算法[J]. 计算机研究与发展, 2019, 56(2): 319–327
HUANG Jipeng, SHI Yinghuan, GAO Yang. Multi-scale faster-RCNN algorithm for small object detection[J]. Journal of computer research and development, 2019, 56(2): 319–327
[8] SONG Ling, XIA Zhimin. Research on improved mask R-CNN network model for human keypoint detection[J]. Computer engineering and applications, 2021, 57(1): 150–160.
[9] 刘学平, 李玙乾, 刘励, 等. 自适应边缘优化的改进YOLOV3目标识别算法[J]. 微电子学与计算机, 2019, 36(7): 59–64
LIU Xueping, LI Yuqian, LIU Li, et al. Improved YOLOV3 target recognition algorithm for adaptive edge optimization[J]. Microelectronics & computer, 2019, 36(7): 59–64
[10] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 779-788.
[11] LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[C]//European Conference on Computer Vision. Cham: Springer, 2016: 21-37.
[12] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[J]. IEEE transactions on pattern analysis and machine intelligence, 2020, 42(2): 318–327.
[13] REDMON J, FARHADI A. YOLOv3: an incremental improvement[EB/OL]. (2018-04-08)[2022-01-01]. https://arxiv.org/abs/1804.02767.
[14] BOCHKOVSKIY A, WANG Chienyao, LIAO Hongyuan. YOLOv4: Optimal Speed and Accuracy of Object Detection[EB/OL]. (2020-04-23)[2022-01-01]. https://arxiv.org/abs/2004.10934.
[15] LIU Shu, QI Lu, QIN Haifang, et al. Path aggregation network for instance segmentation[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 8759-8768.
[16] GE Zheng, LIU Songtao, WANG Feng, et al. YOLOX: exceeding YOLO series in 2021[EB/OL]. (2021-07-18)[2022-01-01]. https://arxiv.org/abs/2107.08430.pdf.
[17] ZHANG Xiangyu, ZHOU Xinyu, LIN Mengxiao, et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 6848-6856.
[18] CHENG Gong, HAN Junwei, ZHOU Peicheng, et al. Multi-class geospatial object detection and geographic image classification based on collection of part detectors[J]. ISPRS journal of photogrammetry and remote sensing, 2014, 98: 119–132.
[19] AGGARWAL V, WANG Wenlin, ERIKSSON B, et al. Wide compression: tensor ring nets[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 9329-9338.
[20] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(9): 1904–1916.
[21] LIN T Y, DOLL?R P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 936-944.
[22] TAN Mingxing, PANG Ruoming, LE Q V. EfficientDet: scalable and efficient object detection[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 10778-10787.
[23] LIU Mingjie, WANG Xianhao, ZHOU Anjian, et al. UAV-YOLO: small object detection on unmanned aerial vehicle perspective[J]. Sensors, 2020, 20(8): 2238.
[24] 王凤随, 陈金刚, 王启胜, 等. 自适应上下文特征的多尺度目标检测算法[J]. 智能系统学报, 2022, 17(2): 276–285
WANG Fengsui, CHEN Jingang, WANG Qisheng, et al. Multi-scale target detection algorithm based on adaptive context features[J]. CAAI transactions on intelligent systems, 2022, 17(2): 276–285
[25] 赵文清, 杨盼盼. 双向特征融合与注意力机制结合的目标检测[J]. 智能系统学报, 2021, 16(6): 1098–1105
ZHAO Wenqing, YANG Panpan. Target detection based on bidirectional feature fusion and an attention mechanism[J]. CAAI transactions on intelligent systems, 2021, 16(6): 1098–1105
[26] WANG Hao, WANG Qilong, GAO Mingqi, et al. Multi-scale location-aware kernel representation for object detection[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 1248-1257.
[27] HU Jie, SHEN Li, ALBANIE S, et al. Squeeze-and-excitation networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2020, 42(8): 2011–2023.
[28] WOO S, PARK J, LEE J Y, et al. CBAM: Convolutional block attention module[C] // Proc of the 15th European Conference on Computer Vision. Munich: Springer, 2018: 3-19
[29] TIAN Zhuoyu, MA Miao, YANG Kaifang. Object detection model for examination classroom based on cascade attention and point supervision mechanism[J]. Journal of software, 2022, 33(7): 2633–2645.
[30] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all You need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. New York: ACM, 2017: 6000-6010.
[31] WANG Xiaolong, GIRSHICK R, GUPTA A, et al. Non-local neural networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 7794-7803.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems