[1]储文娟,李震,黄炜嘉,等.面向失效增强和改进YOLOv8的目标检测[J].智能系统学报,2026,21(2):353-364.[doi:10.11992/tis.202503010]
CHU Wenjuan,LI Zhen,HUANG Weijia,et al.A failure enhancement and improvement of YOLOv8 for target detection[J].CAAI Transactions on Intelligent Systems,2026,21(2):353-364.[doi:10.11992/tis.202503010]
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CHU Wenjuan,LI Zhen,HUANG Weijia,et al.A failure enhancement and improvement of YOLOv8 for target detection[J].CAAI Transactions on Intelligent Systems,2026,21(2):353-364.[doi:10.11992/tis.202503010]
面向失效增强和改进YOLOv8的目标检测
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
21
期数:
2026年第2期
页码:
353-364
栏目:
学术论文—机器学习
出版日期:
2026-03-05
- Title:
- A failure enhancement and improvement of YOLOv8 for target detection
- Keywords:
- computer vision; complex environment; object detection; YOLO; image enhancement; attention mechanism; feature fusion; loss function
- 分类号:
- TP391.41
- 摘要:
- 针对当前在光照、天气、遮挡等复杂背景条件下进行目标检测技术的检测性能较低、泛化能力弱等问题，文章提出一种基于失效增强和改进YOLOv8的目标检测算法（asymptotic structure of YOLO, AS_YOLO）。1）基于复杂场景构建了多种目标单元数据集，并设计面向应用环境的图像失效增强技术；2）引入通道–空间并行注意力机制同时关注复杂环境下目标的特征信息与位置信息；3）采用AFPN结构强化非相邻层级的特征融合效果；4）采用了Inner_IoU(inner intersection over union)损失函数改善现有IoU(intersection over union)损失函数，在不同检测任务中的泛化能力不足的问题，并在WSODD多目标数据集下进行迁移实验。实验结果表明，改进后的算法与基线模型YOLOv8n相比，mAP0.5达到了94.0%，提升12.5百分点，mAP0.95达到了72.5%，提升15.7百分点，具有更好的检测性能。
- Abstract:
- To address the issues of low detection performance and weak generalization ability in target detection under complex background conditions such as illumination, weather, and occlusion, this paper proposes an improved object detection algorithm based on failure augmentation and enhanced YOLOv8 (AS_YOLO). First, a variety of target unit datasets were constructed based on complex military scenarios, and an image failure augmentation technique tailored to the application environment was developed. Second, a channel-spatial parallel attention mechanism was introduced to simultaneously focus on feature and position information of targets in complex environments. Then, the AFPN structure was used to enhance feature fusion of non-adjacent hierarchical layers. Finally, the Inner_IoU loss function was adopted to address the generalization limitations of existing IoU loss functions in different detection tasks. Transfer experiments were conducted on the WSODD multi-target dataset. The experimental results show that the improved algorithm achieves an mAP0.5 of 94.0%, a 12.5 percentage point improvement over the baseline YOLOv8n model, and an mAP0.95 of 72.5%, a 15.7 percentage point improvement, indicating superior detection performance.
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备注/Memo
收稿日期:2025-3-6。
基金项目:国家自然科学基金项目（62276285）；教育部学位与研究生教育发展中心主题案例库项目（ZT-231028914）；江苏省研究生科研与实践创新计划项目（KYCX24-4178）；中国科学院软件研究所合作项目（2205072325）.
作者简介:储文娟，硕士研究生，主要研究方向为视觉目标识别与图像处理。E-mail：2294806304@qq.com。;李震，教授，博士，主要研究方向为可靠性与系统工程。主持国家级项目2项、省部级项目2项、横向项目20余项。E-mail：justlz@just.edu.cn。;黄炜嘉，副教授，博士，主要研究方向为图像处理。主持横向项目1项，参研国家自然科学基金面上项目2项。E-mail：huangweijia@just.edu.cn。
通讯作者:李震. E-mail：justlz@just.edu.cn
更新日期/Last Update:
1900-01-01