Object detection and size estimation algorithm for steel recycling

Journal Information

J. Hefei Univ. Tech. (Nat. Sci.), Mar.2024, Vol.47 No.3: 338-346

DOI: 10.3969/j.issn.1003-5060.2024.03.009

Authors

FAN Binbin $ ^{1,2} $, QIN Xunpeng $ ^{1,2} $, WU Qiang $ ^{1,2} $, WANG Zhe $ ^{1,2} $, BI Jiuju $ ^{1,2} $

(1. School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China; 2. Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan 430070, China)

Abstract and Keywords

Abstract: To solve the problem of inability to accurately obtain the target scrap point cloud for size estimation due to open and complex scenes in the process of steel recycling, a target point cloud extraction algorithm based on the prediction mask generated by the Mask R-CNN model and adaptive color thresholding technique is proposed. For the incomplete edge segmentation of the Mask R-CNN model, the point cloud is intercepted through the prediction frame, the adaptive color filter threshold is used to filter the intercepted point cloud in HSV color space to discretize the non-target point cloud, and finally the target point cloud is segmented through European clustering and dimensionally estimated. The effectiveness of the algorithm is verified by simulating the recovery scene of the B-pillar of the scrapped car and obtaining the point cloud of the B-pillar of the car.

Keywords: binocular stereo vision; instance segmentation; point cloud segmentation; color filtering; steel recycling

作者信息

范彬彬 $ ^{1,2} $，秦训鹏 $ ^{1,2} $，吴强 $ ^{1,2} $，王哲 $ ^{1,2} $，毕玖珺 $ ^{1,2} $

(1. 武汉理工大学汽车工程学院，湖北武汉 430070；2. 现代汽车零部件技术湖北省重点实验室，湖北武汉 430070)

摘要和关键词

摘要: 为解决钢铁回收过程中由于开放式复杂场景而无法准确地获得目标废钢点云进行尺寸估计,文章提出一种基于Mask R-CNN模型预测掩膜自适应颜色阈值的目标点云提取算法。针对Mask R-CNN模型边缘分割的不完整性,通过预测框对点云进行截取,利用自适应颜色滤波阈值对截取的点云进行HSV颜色空间滤波,将非目标点云离散化,最后通过欧式聚类对目标点云进行分割并进行尺寸估计。通过废旧汽车B柱模拟回收场景,获取汽车B柱点云,验证了该算法的有效性。

关键词: 双目立体视觉；实例分割；点云分割；颜色滤波；钢铁回收

Funding

中国博士后科学基金资助项目（2020M682498）；湖北省技术创新重大专项资助项目（2019AAA075；2020BED010）

合肥工业大学学报(自然科学版)

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