基于改进YOLOv8的电梯内电动车检测算法  

Electric vehicle detection algorithm in elevators based on the improved YOLOv8

作　　者：沙彦佑 左官芳 Sha Yanyou;Zuo Guanfang(School of Electronic and Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China;School of Electronic Information Engineering,Wuxi University,Wuxi 214105,China)

机构地区：[1]南京信息工程大学电子与信息工程学院,南京210044 [2]无锡学院电子信息工程学院,无锡214105

出　　处：《电子测量技术》2025年第5期81-91,共11页Electronic Measurement Technology

摘　　要：针对YOLOv8算法在复杂场景下电梯内电动车检测精度的问题,提出了一种以YOLOv8n算法为基础改进的算法。首先,对于主干网络,将C2f模块与通用倒置瓶颈结构融合形成新的C2f_UIB模块来优化计算效率,降低参数量的同时提高全局信息捕获能力,同时在主干网络中添加空间和通道协同注意力模块SCSA,提高特征提取能力和模型的鲁棒性。其次,用改进后的重参数化广义特征金字塔网络DSRepGFPN对颈部网络进行重构,增强跨尺度特征融合能力,提升多尺度目标的检测效果并减小模型的计算复杂度。最后将原有的损失函数CIOU替换为MPDIOU,提高目标框的定位精度,特别是在光照变化和目标遮挡场景中表现出更强的定位与识别能力。实验结果表明,在电梯内电动车数据集中,相较于YOLOv8n,本文所改进的YOLOv8-UAR算法在mAP50上提高了2.5%,在mAP50-95上提高了1.8%,同时检测速度达到94 fps,方便部署在边缘设备上,且更符合电动车进电梯检测的实际应用要求。An improved algorithm based on the YOLOv8n model is proposed to address the challenge of detection accuracy for electric vehicles entering elevators in complex scenarios.First,the backbone network integrates the C2f module with the universal inverted bottleneck structure to form a new C2f_UIB module,optimizing computational efficiency and reducing parameters while improving global information capture capability.Additionally,a spatial-channel synergistic attention(SCSA)module is added to the backbone to enhance feature extraction and model robustness.Second,the neck network is reconstructed using an improved re-parameterized generalized feature pyramid network(DSRepGFPN),which enhances cross-scale feature fusion,improves multi-scale object detection performance,and reduces computational complexity.Finally,the original CIOU loss function is replaced with MPDIOU,improving bounding box localization accuracy,especially in scenarios with lighting variations and object occlusions.Experimental results on an elevator electric vehicle dataset show that the proposed YOLOv8-UAR algorithm achieves a 2.5%improvement in mAP50 and a 1.8%improvement in mAP50-95 compared to YOLOv8n,with a detection speed of 94 fps.This makes it suiTable for deployment on edge devices,aligning better with practical requirements for electric vehicle elevator detection.

关 键 词：YOLOv8 倒置瓶颈结构 注意力机制 重参数化特征金字塔 MPDIoU 电动车检测 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TN98[自动化与计算机技术—计算机科学与技术]