面向密集行人场景的YOLOv8n改进算法  

An improved YOLOv8n algorithm for dense pedestrian scenarios

作　　者：王丽黎[1,2] 樊盼盼 张诗雨 Wang Lili;Fan Panpan;Zhang Shiyu(School of Automation and Information Engineering,Xi’an University of Technology,Xi’an 710048,China;Key Laboratory of Wireless Optical Communication and Network Research,Xi’an 710048,China)

机构地区：[1]西安理工大学自动化与信息工程学院,陕西西安710048 [2]无线光通信与网络研究重点实验室,陕西西安710048

出　　处：《电子技术应用》2025年第2期15-20,共6页Application of Electronic Technique

摘　　要：为了解决传统算法在密集行人场景中识别精度不足和检测不准确的问题,提出一种基于YOLOv8n的改进型密集行人检测模型。首先,引入SPPELAN模块替换骨干网络中的SPPF模块,以提升模型对多尺度目标的特征感知能力。其次,设计一种残差注意力机制,提高模型对细微特征的提取能力,进而提高检测精度。最后通过添加DySample算子、改进的小目标检测层提高模型对小尺度目标的定位识别能力。实验结果显示,改进的模型相较于YOLOv8n在CrowdHuman数据集上的召回率、mAP_(50)和mAP_(50-95)分别提升了2.5%、2.9%和2.4%,并且该模型在WiderPerson和CityPersons数据集上表现优异。实验结果表明,该算法能更好适用于密集行人检测任务。To address the issues of insufficient recognition accuracy and inaccurate detection of traditional algorithms in dense pedestrian scenarios,an improved dense pedestrian detection model based on YOLOv8n is proposed.Firstly,by introducing the SPPELAN module to replace the SPPF module in the backbone network,the model’s ability to perceive features of multi-scale targets is enhanced.Secondly,a residual attention mechanism is devised to improve the model’s ability to capture subtle features,thereby enhancing detection accuracy.Finally,by adding DySample operator and improving the small object detection layer,the model’s ability to locate and recognize small-scale objects is enhanced.Experimental results show that the improved model,compared to YOLOv8n,increases recall rate,mAP_(50),and mAP_(50-95)by 2.5%,2.9%,and 2.4%,respectively,on the CrowdHuman dataset,and performs excellently on the WiderPerson and CityPersons datasets.The results of the experiments show that this algorithm is more effective for dense pedestrian detection tasks.

关 键 词：YOLOv8n 密集行人检测 SPPELAN模块 残差注意力机制 DySample 小目标检测层 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]