基于改进YOLOv8的轻量级装配工件检测算法

doi:10.16182/j.issn1004731x.joss.24-0910

系统仿真学报 ›› 2025, Vol. 37 ›› Issue (12): 3099-3111.doi: 10.16182/j.issn1004731x.joss.24-0910

• 论文 • 上一篇

基于改进YOLOv8的轻量级装配工件检测算法

伍枢珩¹^,², 刘永奎¹^,², 张霖³, 肖莹莹⁴^,⁵, 王力翚⁶

^1.西安电子科技大学机电工程学院，陕西西安 710071
^2.西安电子科技大学高性能电子装备机电集成制造全国重点实验室，陕西西安 710071
^3.北京航空航天大学自动化科学与电气工程学院，北京 100191
^4.北京仿真中心，北京 100854
^5.北京电子工程总体研究所，北京 100854
^6.瑞典皇家理工学院，斯德哥尔摩 25175

收稿日期:2024-08-16 修回日期:2024-10-04 出版日期:2025-12-26 发布日期:2025-12-24
通讯作者: 刘永奎
第一作者简介:伍枢珩(2000-)，男，硕士生，研究方向为计算机视觉和智能机器人。
基金资助:
国家自然科学基金(61973243)

Lightweight Assembly Workpiece Detection Algorithm Based on Improved YOLOv8

Wu Shuheng¹^,², Liu Yongkui¹^,², Zhang Lin³, Xiao Yingying⁴^,⁵, Wang Lihui⁶

^1.School of Mechano-Electronic Engineering, Xidian University, Xi'an 710071, China
^2.State Key Laboratory of Electromechanical Integrated Manufacturing of High-performance Electronic Equipments, Xidian University, Xi'an 710071, China
^3.School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
^4.Beijing Simulation Center, Beijing 100854, China
^5.Beijing Institute of Electronic System Engineering, Beijing 100854, China
^6.KTH Royal Institute of Technology, Stockholm 25175, Sweden

Received:2024-08-16 Revised:2024-10-04 Online:2025-12-26 Published:2025-12-24
Contact: Liu Yongkui

摘要/Abstract

摘要：

针对现有的基于深度学习的目标检测算法在机器人自动装配任务中识别精度低、检测速度慢的问题，提出了一种基于YOLOv8的轻量级装配工件目标检测算法。引入部分卷积PConv对C2f模块进行改进，设计新的Faster_C2f模块，提高了模型的检测速度；采用SIoU损失函数来优化CIoU损失函数的位置预测精度并提高小目标的定位准确性；使用HS-FPN（high-level screening-feature fusion pyramid networks）结构改进Neck部分，减少了模型的计算量，提高了检测速度；结合iRMB模块与EMA注意力机制，提出了iEMA机制。实验结果表明：在自制装配工件数据集上，与原始算法相比，改进算法F1分数提高了3.2%，mAP值提高了1.7%，帧率提高了11%，而模型参数量减少了47%，计算复杂度降低了32%。

关键词: YOLOv8, 注意力机制, 轻量化, 机器人自动装配, 目标检测

Abstract:

To address the issues of low recognition accuracy and slow detection speed with existing deep learning-based object detection algorithms for robotic automatic assembly tasks, a lightweight assembly workpiece object detection algorithm based on YOLOv8 was proposed. The PConv was introduced to improve the C2f module, anda new Faster_C2f module was designed to enhance the detection speed of the model. The SIoU loss function was employed to optimize the location prediction accuracy of the CIoU loss function and improve the localization accuracy of small targets. The high-level screening-feature fusion pyramid networks (HS-FPN) structure was used to improve the Neck part, which significantly reduced the model's computational load and increased the detection speed. By combining the iRMB module with the EMA attention mechanism, an iEMA mechanism was proposed. Experimental results have indicated that on the self-made assembly workpiece dataset, the improved algorithm increases the F1 score by 3.2%, the mAP value by 1.7%, and the FPS by 11%, while reducing the model's parameter count by 47% and the computational complexity by 32% compared with the original algorithm.

Key words: YOLOv8, attention mechanism, light weight, robotic automatic assembly, object detection

中图分类号:

TP391.41

伍枢珩,刘永奎,张霖等 . 基于改进YOLOv8的轻量级装配工件检测算法[J]. 系统仿真学报, 2025, 37(12): 3099-3111.

Wu Shuheng,Liu Yongkui,Zhang Lin,et al . Lightweight Assembly Workpiece Detection Algorithm Based on Improved YOLOv8[J]. Journal of System Simulation, 2025, 37(12): 3099-3111.

图/表 20

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

图11

图12

表1

图13

图14

表2

表3

表4

图15

图16

参考文献 26

[1]	刘检华, 孙清超, 程晖, 等. 产品装配技术的研究现状、技术内涵及发展趋势[J]. 机械工程学报, 2018, 54(11): 1-28.
	Liu Jianhua, Sun Qingchao, Cheng Hui, et al. The State-of-the-art, Connotation and Developing Trends of the Products Assembly Technology[J]. Journal of Mechanical Engineering, 2018, 54(11): 1-28.
[2]	王耀南, 江一鸣, 姜娇, 等. 机器人感知与控制关键技术及其智能制造应用[J]. 自动化学报, 2023, 49(3): 494-513.
	Wang Yaonan, Jiang Yiming, Jiang Jiao, et al. Key Technologies of Robot Perception and Control and Its Intelligent Manufacturing Applications[J]. Acta Automatica Sinica, 2023, 49(3): 494-513.
[3]	Les Tomasz, Kruk Michal, Osowski Stanislaw. Automatic Recognition of Industrial Tools Using Artificial Intelligence Approach[J]. Expert Systems with Applications, 2013, 40(12): 4777-4784.
[4]	Paramarthalingam Arjun, Mirnalinee T T. Machine Parts Recognition and Defect Detection in Automated Assembly Systems Using Computer Vision Techniques[J]. Revista Tecnica De La Facultad De Ingenieria Universidad Del Zulia, 2016, 39(1): 71-80.
[5]	Song Rui, Li Fengming, Quan Wei, et al. Skill Learning for Robotic Assembly Based on Visual Perspectives and Force Sensing[J]. Robotics and Autonomous Systems, 2021, 135: 103651.
[6]	Wang Xi, Jaume Soriano Pinter, Liu Zhihao, et al. A Machine Learning-based Image Processing Approach for Robotic Assembly System[J]. Procedia CIRP, 2021, 104: 906-911.
[7]	Li Jing, Gu Jinan, Huang Zedong, et al. Application Research of Improved YOLO V3 Algorithm in PCB Electronic Component Detection[J]. Applied Sciences, 2019, 9(18): 3750.
[8]	Chen Chengjun, Wang Tiannuo, Li Dongnian, et al. Repetitive Assembly Action Recognition Based on Object Detection and Pose Estimation[J]. Journal of Manufacturing Systems, 2020, 55: 325-333.
[9]	Chen Wenbai, Yang Genjian, Zhang Bo, et al. Lightweight and Fast Visual Detection Method for 3C Assembly[J]. Displays, 2024, 82: 102631.
[10]	Chen Jierun, Kao S H, He Hao, et al. Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2023: 12021-12031.
[11]	Chen Yifei, Zhang Chenyan, Chen Ben, et al. Accurate Leukocyte Detection Based on Deformable-DETR and Multi-level Feature Fusion for Aiding Diagnosis of Blood Diseases[J]. Computers in Biology and Medicine, 2024, 170: 107917.
[12]	Zhang Jiangning, Li Xiangtai, Li Jian, et al. Rethinking Mobile Block for Efficient Attention-based Models[C]//2023 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway: IEEE, 2023: 1389-1400.
[13]	Ouyang Daliang, He Su, Zhang Guozhong, et al. Efficient Multi-scale Attention Module with Cross-spatial Learning[C]//ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Piscataway: IEEE, 2023: 1-5.
[14]	Zhang Haoyang, Wang Ying, Dayoub Feras, et al. VarifocalNet: An IoU-aware Dense Object Detector[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2021: 8510-8519.
[15]	Li Xiang, Wang Wenhai, Wu Lijun, et al. Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection[C]//Proceedings of the 34th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2020: 21002-21012.
[16]	Yu Jiahui, Jiang Yuning, Wang Zhangyang, et al. UnitBox: An Advanced Object Detection Network[C]//Proceedings of the 24th ACM International Conference on Multimedia. New York: ACM, 2016: 516-520.
[17]	Rezatofighi H, Tsoi N, Gwak J Y, et al. Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2019: 658-666.
[18]	Zheng Zhaohui, Wang Ping, Liu Wei, et al. Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2020: 12993-13000.
[19]	Lin T Y, Dollár Piotr, Girshick R, et al. Feature Pyramid Networks for Object Detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2017: 936-944.
[20]	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. Piscataway: IEEE, 2018: 8759-8768.
[21]	Li Yanyu, Hu Ju, Wen Yang, et al. Rethinking Vision Transformers for MobileNet Size and Speed[C]//2023 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway: IEEE, 2023: 16843-16854.
[22]	Zhu Lei, Wang Xinjiang, Ke Zhanghan, et al. BiFormer: Vision Transformer with Bi-level Routing Attention[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2023: 10323-10333.
[23]	Wang C Y, Bochkovskiy A, Liao Hongyuan. YOLOv7: Trainable Bag-of-freebies Sets New State-of-the-art for Real-time Object Detectors[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2023: 7464-7475.
[24]	Wang Chengcheng, He Wei, Nie Ying, et al. Gold-YOLO: Efficient Object Detector via Gather-and-distribute Mechanism[C]//Proceedings of the 37th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2023: 51094-51112.
[25]	Zhao Yian, Wenyu Lü, Xu Shangliang, et al. DETRs Beat YOLOs on Real-time Object Detection[C]//2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2024: 16965-16974.
[26]	Selvaraju R R, Cogswell M, Das A, et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization[C]//2017 IEEE International Conference on Computer Vision (ICCV). Piscataway: IEEE, 2017: 618-626.

参数	设置
训练时长	300
优化器	AdamW
动量	0.9
学习率	0.000 769
权重衰减	0.000 5
图像大小	640×640
批量大小	16

模型	精确率/%	召回率/%	F1值/%	平均精度均值/%	参数量	千兆次浮点计算量	帧率/(帧/s)
Baseline	95.0	93.7	94.3	97.2	5.6×10⁶	19.3	78.7
+ EMA	95.0	93.8	94.3	97.1	5.6×10⁶	19.3	76.2
+ iRMB	93.6	94.1	93.8	96.9	6.6×10⁶	50.3	69.5
+ CA	95.8	93.3	94.5	96.7	5.7×10⁶	19.5	72.1
+ Biformer^[22]	92.6	94.7	93.6	95.8	6.6×10⁶	60.6	71.1
+ iEMA	95.6	95.7	95.6	97.8	5.9×10⁶	19.6	74.6

实验	Faster_C2f	SIoU	HS-FPN	iEMA	精确率/%	召回率/%	F1值/%	平均精度均值/%	参数量	千兆次浮点计算量	帧率/(帧/s)
1					88.0	97.3	92.4	96.1	11.1×10⁶	28.7	67.1
2	√				93.4	92.1	92.7	95.3	9.7×10⁶	24.4	81.4
3		√			93.7	93.4	93.5	96.3	11.1×10⁶	28.7	67.1
4			√		94.1	89.5	91.7	97.1	7.1×10⁶	24.4	75.2
5				√	93.3	92.9	93.0	96.8	11.2×10⁶	29.0	64.1
6	√	√			94.2	94.0	94.1	96.4	9.7×10⁶	24.4	81.5
7	√	√	√		95.0	93.7	94.3	97.2	5.6×10⁶	19.3	78.7
8	√	√	√	√	95.6	95.7	95.6	97.8	5.9×10⁶	19.6	74.6

模型	精确率/%	召回率/%	F1值/%	平均精度均值/%	参数量	千兆次浮点计算量	帧率/(帧/s)
YOLOv5s	87.3	94.2	90.6	94.1	7.2	16.5	73.0
YOLOv7-tiny^[23]	83.9	87.1	85.5	88.9	6.0	13.2	80.0
GOLD-YOLO^[24]	93.1	90.8	91.6	91.2	5.6	12.1	86.4
RT-DERT^[25]	95.3	92.3	93.8	93.8	42.8	130.5	19.3
YOLOv8s	88.0	97.3	92.4	96.1	11.1	28.7	67.1
Ours	95.6	95.7	95.6	97.8	5.9	19.6	74.6

基于改进YOLOv8的轻量级装配工件检测算法

Lightweight Assembly Workpiece Detection Algorithm Based on Improved YOLOv8

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 20

参考文献 26

相关文章 15

编辑推荐

Metrics

本文评价

[1]	江明, 何韬. 基于深度强化学习的带容量约束车辆路径问题求解[J]. 系统仿真学报, 2025, 37(9): 2177-2187.
[2]	姜彦吉, 张颖阳, 董浩, 张晓光, 王美惠. 基于实例关联的暗光下车道线检测[J]. 系统仿真学报, 2025, 37(9): 2188-2199.
[3]	马仑, 杨跃, 王迨贺, 廖桂生, 李幸. 联合自注意力机制与权值共享的人体行为识别模型[J]. 系统仿真学报, 2025, 37(9): 2409-2419.
[4]	鲁斌, 杨烜, 杨振宇, 高啸天. 自适应采样与重影多尺度特征融合的轻量化焊缝缺陷检测[J]. 系统仿真学报, 2025, 37(8): 1978-1990.
[5]	李明煜, 林家泉. 基于YOLOv8-DF的轻量化驾驶员面部目标检测算法[J]. 系统仿真学报, 2025, 37(8): 2103-2114.
[6]	刘子龙, 张磊. 自然环境下改进YOLOv5对小目标苹果的检测[J]. 系统仿真学报, 2025, 37(8): 2124-2138.
[7]	杨路, 裴俊莹. 融合多尺度特征的航拍目标检测算法[J]. 系统仿真学报, 2025, 37(6): 1486-1498.
[8]	王子怡, 张凯, 钱殿伟, 刘玉贞. 一种基于DRL的分布式装备体系优选方法[J]. 系统仿真学报, 2025, 37(6): 1565-1573.
[9]	伍国华, 曾家恒, 王得志, 郑龙, 邹伟. 基于深度强化学习的四旋翼航迹跟踪控制方法[J]. 系统仿真学报, 2025, 37(5): 1169-1187.
[10]	王祥, 谭国真. 基于知识与大语言模型的高速环境自动驾驶决策研究[J]. 系统仿真学报, 2025, 37(5): 1246-1255.
[11]	李杰, 刘扬, 李良, 苏本淦, 魏佳隆, 周广达, 石艳敏, 赵振. 基于跨阶段双分支特征聚合的遥感小目标检测[J]. 系统仿真学报, 2025, 37(4): 1025-1040.
[12]	郑岚月, 张玉洁. 基于改进YOLOv7的交通信号灯检测[J]. 系统仿真学报, 2025, 37(4): 993-1007.
[13]	王贺, 许佳宁, 闫广宇. 基于深度强化学习的AGV行人避让策略研究[J]. 系统仿真学报, 2025, 37(3): 595-606.
[14]	李想, 任晓羽, 周永兵, 张剑. 基于改进D3QN算法的随机工时下柔性综合调度问题研究[J]. 系统仿真学报, 2025, 37(2): 474-486.
[15]	费帅迪, 蔡长龙, 刘飞, 陈明晖, 刘晓明. 舰船防空反导的目标分配方法研究[J]. 系统仿真学报, 2025, 37(2): 508-516.