| 田 勇,周曾鹏,田劲东,胡 超.结合高光谱和机器学习的无线充电金属异物检测[J].电子测量与仪器学报,2022,36(8):238-247 |
| 结合高光谱和机器学习的无线充电金属异物检测 |
| Metal object detection in wireless charging systems combininghyperspectral imaging and machine learning |
| |
| DOI： |
| 中文关键词: 无线充电 金属异物检测 电动汽车 高光谱成像 机器学习 |
| 英文关键词:wireless charging metal object detection electric vehicles hyperspectral imaging machine learning |
| 基金项目:广东省重点领域研发计划项目（2020B0404030004）资助 |
|
|
|
|
| 摘要点击次数: 822 |
| 全文下载次数: 690 |
| 中文摘要: |
| 金属异物侵入会造成无线充电系统效率和稳定性降低,并且可能引发安全事故,因此必须进行金属异物检测。 针对现
有技术存在检测盲区以及无法检测微小异物的问题,提出一种深度学习目标分割与机器学习目标分类相结合的金属异物检测
方法。 首先采用 YOLO v3 网络对充电区域 RGB 图像进行异物目标分割,然后通过支持向量机对各个目标区域对应的高光谱图
像进行分类,最后搭建实验平台验证方法的有效性。 结果表明,该方法不仅能够检测螺母和回形针等微小金属异物,而且具有
检测包裹金属异物的潜能;与仅采用支持向量机进行逐像素检测相比,该方法的检测速度提升了约 38. 9%。 |
| 英文摘要: |
| The intrusion of metal foreign objects will lower the efficiency and stability of wireless power transfer systems, even causing
safety issues, thus it is extremely essential to achieve metal object detection. Aiming at the problem that existing technologies are
subjected to blind-zone and cannot detect small foreign objects, a metal object detection method that combines deep learning-based object
segmentation and support vector machine (SVM)-based object classification is proposed. First, object segmentation is performed using a
YOLO v3 neural network based on RGB image of the charging area. Then the corresponding hyperspectral images of each object region
are classified by the SVM. Finally, an experimental platform is built to verify the effectiveness of the proposed method. Results show that
the proposed method not only detects tiny metal object such as a screw nut and a paper clip, but also has the potential to detect metal
objects wrapped by non-metal material. Compared with the pixel-by-pixel detection using SVM alone, the proposed method improves the
detection speed by about 38. 9%. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
