| 基于改进YOLOv7的小目标检测 |
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| 引用本文： | 戚玲珑,高建瓴. 基于改进YOLOv7的小目标检测[J]. 计算机工程, 2023, 49(1): 41-48. DOI: 10.19678/j.issn.1000-3428.0065942 |
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| 作者姓名： | 戚玲珑 高建瓴 |
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| 作者单位： | 贵州大学 大数据与信息工程学院,贵阳 550025 |
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| 基金项目： | 国家自然科学基金（62166006）。 |
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| 摘 要： | 目前的目标检测技术已趋于成熟,但小目标检测仍是研究的难点。针对目标检测过程中小目标检测更容易出现漏检等问题,提出一种改进的YOLOv7目标检测模型。结合特征分离合并思想,对YOLOv7网络模型中的MPConv模块进行改进,以减少网络特征处理过程造成的特征损失,并通过实验确定放置改进MPConv模块的最佳位置。由于小目标检测过程中容易出现漏检的现象,利用ACmix注意力模块提高网络对小尺度目标的敏感度,降低噪声所带来的影响。在此基础上,使用SIoU替换原YOLOv7网络模型中的CIoU来优化损失函数,减少损失函数自由度,提高网络鲁棒性。在Okahublot公开的FloW-Img子数据集上进行实验,结果表明,对于数据集中的密集、小目标和超小目标三种情况的图片,改进后的YOLOv7网络模型相比原网络,漏检情况得到明显改善,且mAP达到71.1%,相比基线YOLOv7网络模型提升了4个百分点,检测效果优于原网络模型与传统经典目标检测网络模型。
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| 关 键 词： | 目标检测技术 小目标检测 YOLOv7网络模型 注意力模块 损失函数 |
| 收稿时间： | 2022-10-09 |
| 修稿时间： | 2022-11-11 |
Small Object Detection Based on Improved YOLOv7 |
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| QI Linglong,GAO Jianling. Small Object Detection Based on Improved YOLOv7[J]. Computer Engineering, 2023, 49(1): 41-48. DOI: 10.19678/j.issn.1000-3428.0065942 |
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| Authors: | QI Linglong GAO Jianling |
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| Affiliation: | College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China |
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| Abstract: | Despite advancements in object detection technology, Small Object Detection(SOD) is still difficult to research.To address the challenge of easily missing detection in the process of object detection, this study proposes an improved YOLOv7 object detection model.Firstly, the MPConv module in the YOLOv7 model is improved by combining feature separation with merge, to reduce the feature loss caused by the process of network feature processing. The optimal position of the improved MPConv module is determined through experiments.Secondly, due to the phenomenon of missing detection in SOD, the algorithm uses the ACmix attention module to increase the sensitivity of the network to small-scale targets and reduce the influence caused by noise.Finally, SIoU is used to replace CIoU in the original YOLOv7 network model to optimize the loss function, reduce the freedom of the loss function, and improve the network robustness.Compared with the original network, the improved YOLOv7 network model can improve the missing detection situation of the images in the data set of the dense, small target, and ultra-small target by experimental comparison with the FloW-Img sub-dataset published by Okahublot.The results show that the mAP of the improved YOLOv7 network model can reach 71.1%, 4 percentage points higher than that of the baseline YOLOv7 network model, and the detection effect is better than that of the original network model and traditional classical target detection networks model. |
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| Keywords: | object detection technology Small Object Detection(SOD) YOLOv7 network model attention module loss function |
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