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| Lightweight object detection based on split attention and linear transformation |
Yan ZHANG1,2( ),Jing-xue SUN1,Ye-mei SUN1,2,*(),Shu-dong LIU1,2,Chuan-qi WANG3 |
1. School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China
2. Tianjin Intelligent Elderly Care and Health Service Engineering Research Center, Tianjin 300384, China
3. Tianjin Keyvia Electric Limited Company, Tianjin 300392, China |
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Abstract To meet the real-time and model lightweight requirements of target detection and improve the accuracy of object detection, a lightweight target detection algorithm PG-YOLOv5 based on pyramid split attention and linear transformation was proposed. The feature fusion module in YOLOv5 was optimized by PG-YOLOv5. First, the pyramid split attention module was used to capture the spatial information of feature maps at different scales to enrich the feature space, thus the multi-scale feature representation ability of the network and the accuracy of object detection were improved. Then, the GhostBottleNeck module based on linear transformation was used to combine a small amount of original feature maps with those obtained from linear transformation, which reduced the number of model parameters effectively. The mean average precision of the algorithm increased from 81.2% of YOLOv5L to 85.7% of PG-YOLOv5, and the number of parameters of PG-YOLOv5 was 36% lower than that of YOLOv5L. The PG-YOLOv5 was deployed on Jetson TX2 and an object detection software was designed. Experimental results showed that the image processing speed of the target detection system based on Jetson TX2 was 262.1 ms/frame, and the mean average precision of PG-YOLOv5 was 85.2%. Compared with the YOLOv5L original model, PG-YOLOv5 is more suitable for edge deployment.
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Received: 26 June 2022
Published: 30 June 2023
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| Fund: 国家重点研发计划资助项目(2021YFB3301600)；天津市科技计划资助项目(22YDTPJC00840) |
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Corresponding Authors:
Ye-mei SUN
E-mail: zhangyan@tcu.edu.cn;sunyemei1216@163.com
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基于分割注意力与线性变换的轻量化目标检测
为了满足目标检测的实时性和模型轻量化需求，提高目标检测精度，对YOLOv5中的特征融合模块进行优化，提出基于金字塔分割注意力与线性变换的轻量化目标检测算法PG-YOLOv5. 利用金字塔分割注意力模块，捕获不同尺度特征图的空间信息以丰富特征空间，提升网络的多尺度特征表示能力，提高目标检测的精度. 利用基于线性变换的GhostBottleNeck模块，以少量原始特征图与线性变换得到的特征图相结合的方式，有效减少模型参数量. 算法的平均精度均值从YOLOv5L的81.2%提高到PG-YOLOv5的85.7%，PG-YOLOv5的参数量比YOLOv5L的下降了36%. 将PG-YOLOv5部署到Jetson TX2，并编写目标检测软件. 实验结果表明，基于Jetson TX2的目标检测系统的图像处理速度为262.1 ms/帧，PG-YOLOv5的平均精度均值为85.2%；与YOLOv5原始模型相比，PG-YOLOv5更适合边缘端部署.
关键词：
目标检测,
金字塔分割注意力,
线性变换,
轻量化,
YOLO
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|
| [1] |
张德祥, 王俊, 袁培成 基于注意力机制的多尺度全场景监控目标检测方法[J]. 电子与信息学报, 2022, 44 (9): 3249- 3257
ZHANG De-xiang, WANG Jun, YUAN Pei-cheng Object detection method for multi-scale full-scene surveillance based on attention mechanism[J]. Journal of Electronics and Information Technology, 2022, 44 (9): 3249- 3257
|
|
|
| [2] |
袁益琴, 何国金, 王桂周, 等 背景差分与帧间差分相融合的遥感卫星视频运动车辆检测方法[J]. 中国科学院大学学报, 2018, 35 (1): 50- 58
YUAN Yi-qin, HE Guo-jin, WANG Gui-zhou, et al A background subtraction and frame subtraction combined method for moving vehicle detection in satellite video data[J]. Journal of University of Chinese Academy of Sciences, 2018, 35 (1): 50- 58
|
|
|
| [3] |
ZHU J, ZOU H, ROSSET S, et al Multi-class AdaBoost[J]. Statistics and its Interface, 2009, 2: 349- 360
doi: 10.4310/SII.2009.v2.n3.a8
|
|
|
| [4] |
GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE, 2014: 580-587.
|
|
|
| [5] |
GIRSHICK R. Fast R-CNN [C]// Proceedings of the IEEE International Conference on Computer Vision. Santiago: IEEE, 2015: 1440-1448.
|
|
|
| [6] |
REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Vegas: IEEE, 2016: 779-788.
|
|
|
| [7] |
REDMON J, FARHADI A. YOLO9000: better, faster, stronger [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 7263-7271.
|
|
|
| [8] |
王立辉, 杨贤昭, 刘惠康, 等 基于GhostNet与注意力机制的行人检测跟踪算法[J]. 数据采集与处理, 2022, 37 (1): 108- 121
WANG Li-hui, YANG Xian-zhao, LIU Hui-kang, et al Pedestrian detection and tracking algorithm based on GhostNet and attention mechanism[J]. Journal of Data Acquisition and Processing, 2022, 37 (1): 108- 121
doi: 10.16337/j.1004-9037.2022.01.009
|
|
|
| [9] |
WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module [C]// Proceedings of the European Conference on Computer Vision. [S.l.]: Springer, 2018: 3-19.
|
|
|
| [10] |
ZHANG H, ZU K, LU J, et al. EPSANet: an efficient pyramid squeeze attention block on convolutional neural network[C]// Proceedings of the Asian Conference on Computer Vision, 2022: 1161-1177.
|
|
|
| [11] |
TAN M, LE Q V. EfficientNet: rethinking model scaling for convolutional neural networks [C]// Proceedings of the 36th International Conference on Machine Learning. [S.l.]: PMLR, 2019: 6105-6114.
|
|
|
| [12] |
LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection [C]// Proceedings of the IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 2980-2988.
|
|
|
| [13] |
ZHANG X, ZHOU X, LIN M, et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 6848-6856.
|
|
|
| [14] |
HAN K, WANG Y, TIAN Q, et al. GhostNet: more features from cheap operations [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 1580-1589.
|
|
|
| [15] |
FU C Y, LIU W, RANGA A, et al. DSSD: deconvolutional single shot detector [EB/OL]. (2017-01-23). https://arxiv.org/pdf/1701.06659.pdf.
|
|
|
| [16] |
ZHANG Z, QIAO S, XIE C, et al. Single-shot object detection with enriched semantics [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 5813–5821
|
|
|
| [17] |
ZHANG S, WEN L, BIAN X, et al. Single-shot refinement neural network for object detection [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 4203-4212.
|
|
|
| [18] |
DAI J, LI Y, HE K, et al. R-FCN: object detection via region-based fully convolutional networks [C]// Proceedings of the 30th International Conference on Neural Information Processing Systems. [S.l.]: CAI, 2016: 379-387.
|
|
|
| [19] |
HAO S, WANG Z, SUN F Stacked pyramid attention network for object detection[J]. Neural Processing Letters, 2022, 54: 2759- 2782
doi: 10.1007/s11063-021-10505-x
|
|
|
| [20] |
DUAN Q, PING K, LI F, et al. Method of safety helmet wearing detection based on key-point estimation without anchor [C]// 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing. Chengdu: IEEE, 2020: 93-96
|
|
|
| [21] |
REN S, HE K, 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: 1137-1149.
|
|
|
| [22] |
REDMON J, FARHADI A. YOLOv3: an incremental improvement [EB/OL]. (2018-04-08). https://arxiv.org/pdf/1804.02767.
|
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