基于改进YOLOv8s的人脸痤疮小目标检测

doi:10.16088/j.issn.1001-6600.2024110101

摘要/Abstract

摘要： 人脸痤疮的自动检测是实现痤疮精准诊疗的关键,而现有方法仍然存在严重的痤疮小目标漏检和误检问题。为实现更准确的痤疮检测,本文提出一种改进的YOLOv8s算法。首先,将YOLOv8s的主干网络改进为一种与Transformer融合的混合主干网络,兼顾卷积神经网络捕获局部细节信息和Transformer维持全局特征信息的优点,显著提高小痤疮目标的特征提取和表征能力。其次,改进YOLOv8s颈部网络的特征融合方式,通过增加多尺度通道注意力模块聚合多尺度上下文信息,以调整各尺度特征权重,缓解特征内容的语义与尺度不一致问题。在公开和自建的人脸痤疮数据集上的实验表明,相比当前最优的痤疮检测算法DSDH,本文方法在检测精度mAP上分别提高1.20和5.24个百分点,检测速度分别提高46.3和47.6 frame/s。

关键词: 人脸痤疮, YOLOv8s, 小目标检测, Transformer, 多尺度特征融合

Abstract: Automated facial acne detection is crucial for precise clinical diagnosis and treatment. However, existing methods still suffer from significant issues of missed detection and false detection of small acne targets. For more accurate acne detection, an enhanced YOLOv8s method with two key modifications is proposed in this paper. Firstly, the original backbone network of YOLOv8s is improved into a hybrid backbone network that integrates with Transformer. This improvement effectively combines the advantages of convolutional neural network capturing local detail information and Transformers maintaining global feature information, significantly enhancing the feature extraction and representation capabilities for small acne objects. Secondly, a multi-scale channel attention module is integrated into the neck network, enabling adaptive feature weight adjustment through cross-scale context aggregation, thereby mitigating semantic-scale inconsistency. Experiments on both public and self-built facial acne datasets demonstrate that, compared with the current state-of-the-art DSDH method, the proposed method achieves mAP improvements of 1.20% and 5.24% on respective datasets, with corresponding detection speed increases of 46.3 and 47.6 frame/s.

Key words: facial acne, YOLOv8s, small object detection, Transformer, multi-scale feature fusion

中图分类号: TP391.41

刘廷汉, 梁艳, 黄鹏升, 闭金杰, 黄守麟, 李廷会. 基于改进YOLOv8s的人脸痤疮小目标检测[J]. 广西师范大学学报（自然科学版）, 2025, 43(5): 114-129.

LIU Tinghan, LIANG Yan, HUANG Pengsheng, BI Jinjie, HUANG Shoulin, LI Tinghui. Facial Acne Detection for Small Object Based on Improved YOLOv8s[J]. Journal of Guangxi Normal University(Natural Science Edition), 2025, 43(5): 114-129.

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