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| 基于改进特征金字塔的小目标增强检测算法 |
| Small object enhancement detection algorithm based on improved feature pyramid |
| 投稿时间：2022-03-06 |
| DOI：10.3969/j.issn.1005-5630.2022.005.002 |
| 中文关键词: 特征金字塔 小目标检测 特征增强 特征融合 |
| 英文关键词:feature pyramid network small object detection feature enhancement feature fusion |
| 基金项目:国家自然科学基金（61975125） |
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| 摘要点击次数: 2802 |
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| 中文摘要: |
| 小尺寸的物体由于其在图像中分辨率相对较低的原因，在检测任务中容易被丢失和误判。针对目前目标检测算法对小尺寸目标检测精确度远低于其他尺寸目标检测精度的问题加以改进，将小尺寸目标特征增强融入特征金字塔结构。利用多尺度特征融合的特征增强能力丰富小尺寸目标特征层的特征信息，从而使小尺寸目标检测精准度得到提升。将改进特征金字塔结构应用于YOLOv3网络，实验对比研究表明，小尺寸目标检测精准度可以达到0.179，较原网络提升了22.6%。 |
| 英文摘要: |
| Small objects are easy to be lost and misjudged in the detection task because of their relatively low resolution in the image. Aiming at the problem that the detection accuracy of small-scale targets in the current target detection algorithm is much lower than that of other sizes, the feature enhancement of small-scale targets is integrated into the feature pyramid structure to avoid the lack of small-scale feature information. The feature enhancement ability of multi-scale feature fusion is used to enrich the feature information of small-scale target feature layer, so as to improve the accuracy of small-scale target detection. The improved feature pyramid structure is applied to YOLOv3 network. The experimental comparative study shows that the detection accuracy of small-scale targets can reach 0.179, which is 22.6% higher than the original network. |
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