改进YOLOv8的电动自行车电池检测算法  

Improved YOLOv8 electric bicycle battery detection algorithm

作　　者：帅勃宇 张雅丽[1] Shuai Boyu;Zhang Yali(School of Information Network Security,People′s Public Security University of China,Beijing 100038,China)

机构地区：[1]中国人民公安大学信息网络安全学院,北京100038

出　　处：《电子测量技术》2025年第5期147-155,共9页Electronic Measurement Technology

基　　金：中国人民公安大学安全防范工程双一流创新研究专项(2023SYL08)资助。

摘　　要：针对电动自行车电池样式单一,特征信息少,应用场景单一问题,重点关注模型轻量化,提出了一种基于视频监控的改进YOLOv8的电动自行车电池检测算法——PSPG-YOLO。在网络中特征提取方面,设计了一种多分支的PStarblock结构优化C2f模块,在Starblock的基础上进一步降低模型复杂度,增强特征信息表达能力;在多尺度融合特征金字塔方面,应用具有共享参数的空洞卷积改进SPPF结构,有效增大了感受野,保留了更多的细节特征信息;在检测头方面,提出了一种超轻量化共享卷积检测头GSPH,应用可共享参数的部分卷积,大幅降低模型复杂度的同时能够动态调整锚点和步长,自动调整内部参数,从而提高对不同尺度特征图的适应能力。在专门针对电动自行车电池的自建数据集上实验表明:PSPG-YOLO相较于基线模型YOLOv8n在计算量、参数量分别下降57%和43%的同时mAP50值提高0.8,在与其他主流检测模型的对比中,综合性能最佳,为目前电动自行车电池违规入户管理提供了一种有效的解决方式。In order to solve the problems of single battery style,less feature information and limited academic scenario,we focus on lightweight detection algorithm,and propose an electric bicycle battery detection algorithm based on an improved version of YOLOv8,termed PSPG-YOLO.In terms of feature extraction within the network mesh,have designed a multi-branch PStarblock to optimize C2f module enhancing the feature-information representational capacity with a simpler module.For multi-scale fusion feature pyramid,dilated convolution with shared parameters is employed to refine the SPPF structure,increasing receptive fields while preserving more detailed feature information.regarding detection head design,we introduce a GSPH.By leveraging shared convolution alongside partial convolution techniques,we reduce model complexity,additionally,both anchor point and stride can be dynamically adjusted;internal parameters are automatically calibrated to enhance adaptability across various scale feature maps.Experiments conducted on a self-constructed data-set specifically targeting electric bicycle batteries demonstrate that,compared to the baseline model YOLOv8n,PSPG-YOLO achieves reductions in FLOPs by 57%and in params by 43%,and improved mAP50 values by 0.8,yielding superior overall performance relative to other mainstream detection models,providing an efficient management method for electric bicycle battery illegal entry into households.

关 键 词：电动自行车电池检测 YOLOv8 轻量级 深度学习 

分 类 号：TN911[电子电信—通信与信息系统]