| 王 呈,黄义超,杨桂锋.基于空间特征融合的车间作业工具检测算法[J].电子测量与仪器学报,2023,37(3):39-49 |
| 基于空间特征融合的车间作业工具检测算法 |
| Workshop tool detection algorithm based on spatial feature fusion |
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| DOI： |
| 中文关键词: 工具检测 帧差法 双通道 注意力 |
| 英文关键词:tool detection frame difference method dual channel attention |
| 基金项目:国家自然科学基金面上项目(61973137)、近地面探测技术重点实验室预研基金(6142414220203)项目资助 |
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| 摘要点击次数: 674 |
| 全文下载次数: 629 |
| 中文摘要: |
| 手和工具的交互是区分车间人员作业行为的关键信息。 为防止泵件装配工序错漏,达到实时监测的目的,提出基于空
间特征融合的车间作业工具检测算法。 首先,为了提高对目标的定位能力和检测精度,基于帧差法分割前景中的手部运动区
域,获得具有运动空间特征的纹理图像,结合装配过程的 RGB 图像构成目标检测网络的双通道输入。 设计空间感知模块实现
双通道输入的空间特征融合,获得全局空间信息。 利用特征增强模块融合全局空间信息和深层语义信息,加强显著位置的特征
响应。 然后,采用 ESNet(enhance shuffleNet)重构主干网络,基于深度可分离卷积实现多尺度特征提取,提高检测速度。 最后,
针对图像背景中局部元素变化问题,采用 CutOut 数据增强方法,提高模型抗干扰能力。 实验结果表明,本文所提算法有效降低
了误检率,较传统 YOLOv5s 的 mAP 提高 6. 4%,能够快速准确检测车间人员作业时使用的工具。 |
| 英文摘要: |
| The interaction of hands and tools is the key information to distinguish the behavior of workers. To prevent the errors and
omissions of the process in the assembly of pumps and achieve the purpose of real-time monitoring, a workshop tool detection algorithm
based on spatial feature fusion is proposed. First, in order to improve the localization ability and detection accuracy of the object, the
hand motion region in the foreground is segmented based on the frame difference method to obtain a texture map with hand spatial
information, which is combined with RGB images of the assembly process to form a dual channel inputs to the object detection network.
The spatial perception module is designed to realize the spatial feature fusion of the dual channel inputs and obtain the global spatial
information. The feature enhancement module is proposed to mix the global spatial information and deep semantic information to enhance
the feature response at salient locations. Then, the ESNet (enhance shuffleNet) is used to reconstruct the backbone network and form a
multi-scale feature extraction module by deep separable convolution to improve the detection speed. Finally, in view of the local elements
change both in the foreground and the background, the CutOut data enhancement method is used to improve the anti-interference
capability. The experimental results show that the proposed algorithm can effectively reduce the false detection rate, and improve the
mAP by 6. 4% compared with the traditional YOLOv5s. The method can quickly and accurately detect the tools used by shop workers. |
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