| 刘 鹏,毕誉轩,张天翼,史佳霖.注意力机制优化的全尺寸目标检测方法[J].电子测量与仪器学报,2023,37(2):193-203 |
| 注意力机制优化的全尺寸目标检测方法 |
| Full-size object detection method optimized by attention mechanism |
| |
| DOI： |
| 中文关键词: 目标检测 YOLOv3 注意力机制 FPN |
| 英文关键词:object detection YOLOv3 attention mechanism FPN |
| 基金项目:吉林省科技发展计划项目(20210201021GX)资助 |
|
|
|
|
| 摘要点击次数: 694 |
| 全文下载次数: 940 |
| 中文摘要: |
| 针对现有目标检测算法全尺寸目标检测精度低的问题,提出了一种改进的基于 YOLOv3 模型的全尺寸目标检测算法。
该方法设计了一种全新的通道自适应递归 FPN 网络架构,提出了一种基于通道注意力的递归金字塔模型,提高了 YOLOv3 的
特征提取能力和不同尺度目标的检测能力。 同时在训练过程中引入损失函数转换,解决了训练过程中动态参数不优化的问题。
与其他主流目标检测算法相比,本文提出的改进模型在小尺寸目标、大尺寸目标与复杂背景多尺寸目标的检测精度分别提高了
5. 6%、2. 6%、1. 6%。 实验结果表明,本文提出的方法检测精度显著提升。 |
| 英文摘要: |
| Aiming at the problem that existing object detection algorithms have low accuracy in full-size object detection, this paper
proposes an improved full-size object detection algorithm based on the YOLOv3 model. In the method, a new adaptive recursive FPN
network architecture is designed, and a recursive pyramid model based on channel attention is proposed to improve the feature extraction
ability of YOLOv3 and the detection ability of objects at different scales. At the same time, loss function transformation is introduced in
the training process to solve the problem of dynamic parameters that is not being optimized in the training process. Compared with other
mainstream object detection algorithms, the accuracy of small-size objects, large-size objects and multi-size objects with complex
backgrounds respectively improved by 5. 6%, 2. 6%, and 1. 6%. Experimental results show that the detection accuracy of the proposed
method is significantly improved. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
