| 王朕,叶文华,陈煜昊,梁睿君.复杂背景下退役圆柱锂电池轮廓精确提取与位姿检测方法[J].电子测量与仪器学报,2024,38(5):119-129 |
| 复杂背景下退役圆柱锂电池轮廓精确提取与位姿检测方法 |
| Accurate contour extraction and pose detection method for retiredcylindricallithium batteries in complex backgrounds |
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| DOI： |
| 中文关键词: 机器视觉 Lambert漫反射模型 弗雷歇距离 轮廓提取 位姿检测 |
| 英文关键词:machine vision Lambert diffuse reflection model Fr-chet distance contour extraction pose detection |
| 基金项目:江苏省重点研发计划（BE2022845）项目资助 |
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| Author | Institution |
| Wang Zhen | School of Mechatronic Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China |
| Ye Wenhua | School of Mechatronic Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China |
| Chen Yuhao | School of Mechatronic Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China |
| Liang Ruijun | School of Mechatronic Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China |
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| 中文摘要: |
| 针对退役圆柱动力锂电池自动化拆解过程中存在的成像环境复杂、电池不规则形变和金属表面不均匀漫反射等复杂情形，现有视觉识别方法无法准确提取轮廓与位姿信息问题，提出基于弗雷歇距离相似函数的轮廓精确提取和基于矩形度与边缘形态特征的位姿检测方法。通过建立圆柱锂电池Lambert漫反射模型和运用形态学运算方法得到锂电池粗定位轮廓，并根据弗雷歇距离定义的相似度函数，对粗定位图像内各像素带归类完成轮廓精确提取。随后根据圆柱锂电池正负极端特征，通过自适应阈值分割算法提取正负极端ROI区域特征轮廓，最后对比两端区域矩形度数值计算出锂电池位姿信息。实验结果显示：在自建包含形变、腐蚀锈斑和光照不均情形下的退役圆柱锂电池图像数据集中，所提方法对不同型号和位姿下的锂电池识别均有较高精度，其直径长度检测误差小于3%，位姿检测正确率高于94%，能够满足实际自动化拆解检测需求。 |
| 英文摘要: |
| In response to the complex imaging environment, irregular deformation of batteries, and uneven diffuse reflection of metal surfaces encountered during the automated disassembly process of retired cylindrical power lithium batteries, existing visual recognition methods are unable to accurately extract contour and pose information. We propose an accurate contour extraction method based on the Fr-chet distance similarity function and a pose detection method based on rectangles and edge morphology features. By establishing a Lambert diffuse reflection model for cylindrical lithium batteries and using morphological operation methods to obtain the rough localization contour of lithium batteries, as well as based on the similarity function defined by the Fr-chet distance, the contour is accurately extracted by classifying each pixel band in the rough localization image; Subsequently, utilizing the positive and negative terminal features of cylindrical lithium batteries, feature contours of the positive and negative terminal ROI regions are extracted employing an adaptive threshold segmentation algorithm. Finally, by comparing the rectangular values of the two end regions, the pose information of the lithium battery can be calculated. The experimental results show that in the Self-built retired cylindrical lithium battery image dataset that includes deformation, corrosion rust spots, and uneven lighting conditions, the proposed method has high accuracy in identifying lithium batteries of different models and poses. The diameter length detection error is less than 3%, and the pose detection accuracy is higher than 94%, which can meet the actual needs of automated disassembly and detection. |
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