首页|多级解码神经网络用于滚珠丝杠点蚀检测

多级解码神经网络用于滚珠丝杠点蚀检测

扫码查看
由于滚珠丝杠点蚀区域小,环境干扰严重,缺陷难以及时检测.所以提出了一种多级解码神经网络,实现滚珠丝杠点蚀缺陷的分割.该网络由编码器、多级解码器和多尺度注意力模块组成.编码器由Resnet34组成,并引入Ghost模块构建了轻量化的多级解码器.为了融合多尺度特征并过滤冗余信息,设计了多尺度注意力模块.采用二值交叉熵函数,IOU和SSIM函数组成的混合损失函数训练网络.在滚珠丝杠缺陷数据集上做了实验,多级解码神经网络在maxFβ指标上达到了0.770 3,与其他方法相比,该网络取得了更好的分割结果,并且单张图片处理时间为26 ms.为滚珠丝杠点蚀缺陷实时分割提供了一种新的方法.
Multi-level decoding neural network for pitting detection of ball screw
Due to the small pitting area of the ball screw and the serious environmental interference,defects are difficult to detect in time.Therefore,a Multi-level decoding neural network is proposed to realize the segmentation of pitting defects in ball screws.The network consists of an encoder,a multi-level decoder and a Multi-scale Attention module.The encoder is composed of Resnet34,and the Ghost module is introduced to build a lightweight multi-level decoder.In order to fuse multi-scale features and filter redundant information,the Multi-scale Attention module is designed.A hybrid loss function composed of BCE function,IOU and SSIM function is used to train the network.Experiments on the ball screw defect dataset show that Multi-level decoding neural network achieves 0.770 3 in the maxFβ metrics,compared with other methods,which achieves better segmentation results,and the processing time of a single image is 26 ms.It provides a new method for real-time segmentation of ball screw pitting defects.

ball screwdefect detectionneural networkimage segmentation

赵慧锋、李铁军

展开 >

沈阳化工大学装备可靠性研究所 沈阳 110142

沈阳化工大学机械与动力工程学院 沈阳 110142

滚珠丝杠 缺陷检测 神经网络 图像分割

国家自然科学基金辽宁省重点(一般)项目

52275156LJKZ0435

2024

电子测量技术
北京无线电技术研究所

电子测量技术

CSTPCD北大核心
影响因子:1.166
ISSN:1002-7300
年,卷(期):2024.47(1)
  • 22