| 改进YOLOv4-tiny网络的狭小空间目标检测方法 |
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| 引用本文： | 王长清,贺坤宇,蒋帅. 改进YOLOv4-tiny网络的狭小空间目标检测方法[J]. 计算机工程与应用, 2022, 58(10): 240-248. DOI: 10.3778/j.issn.1002-8331.2112-0593 |
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| 作者姓名： | 王长清 贺坤宇 蒋帅 |
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| 作者单位： | 河南师范大学 电子与电气工程学院，河南 新乡 453007 |
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| 摘 要： | 针对狭小空间中目标相互遮挡导致轻型检测网络存在大量漏检、分类错误等问题，基于YOLOv4-tiny提出一种自适应非极大抑制（adaptive non-maximum suppression，A-NMS）的多尺度检测方法。在骨干网络引入大尺度特征图优化策略和金字塔池化模型，增强遮挡目标显著区域特征；设计内嵌空间注意力的双路金字塔特征融合网络，提升浅层细节特征与高级语义信息的融合能力；提出区域目标密度与边界框中心距离因子相关联的动态NMS阈值设定方法，并在后处理阶段代替传统IoU-NMS算法，进一步减少漏检。实验结果表明，与YOLOv4-tiny算法相比，改进算法在公开数据集PASCAL VOC07+12和自制数据集上mAP值分别提高2.84个百分点和3.06个百分点，FPS保持在87.9，对遮挡目标的检测能力显著提升，满足移动端对狭小复杂场景实时检测的需求。
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| 关 键 词： | 狭小空间 遮挡目标检测 YOLOv4-tiny 空间注意力 多尺度特征融合 自适应非极大抑制 |
Narrow Space Object Detection Method by Improved YOLOv4-tiny Network |
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| WANG Changqing,HE Kunyu,JIANG Shuai. Narrow Space Object Detection Method by Improved YOLOv4-tiny Network[J]. Computer Engineering and Applications, 2022, 58(10): 240-248. DOI: 10.3778/j.issn.1002-8331.2112-0593 |
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| Authors: | WANG Changqing HE Kunyu JIANG Shuai |
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| Affiliation: | School of Electrical and Electronic Engineering, Henan Normal University, Xinxiang, Henan 453007, China |
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| Abstract: | Aiming at the problems of a large number of missed detections and classification errors in the light detection network due to the mutual occlusion of objects in narrow spaces, an adaptive non-maximum suppression（A-NMS） multiscale detection method based on YOLOv4-tiny network is proposed. A large-scale feature map optimization approach and a pyramid pooling model are incorporated into the backbone network to enhance the major regional features of hidden objects. To improve the fusing of shallow detail data with high-level semantic information, a two-way pyramidal feature fusion network with embedded spatial attention is designed. A dynamic NMS threshold setting method that correlates the regional object density with the distance factor of the center of the bounding box is proposed and replaces the traditional IoU-NMS algorithm in the post-processing stage to further reduce the missed detection. The experimental results show that compared with the YOLOv4-tiny algorithm, the improved algorithm improves the mAP value by 2.84 percentage points and 3.06 percentage points on the public dataset PASCAL VOC07+12 and the self-made dataset, respectively, while the FPS remains at 87.9, the detection capability of occluded objects is significantly improved to meet the demand of mobile terminals for real-time detection of narrow and complex scenes. |
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| Keywords: | narrow space occluded object detection YOLOv4-tiny spatial attention multi-scale feature fusion adaptive non-maximum suppression |
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