Li Helin, Lin Bin, Zhang Chen, Xu Liang, Sui Tianyi, Wang Yang, Hao Xinquan, Lou Deyu, Li Hongyu
School of Mechanical Engineering, Tianjin University, Tianjin 300072, China.
Science and Technology of Advanced Functional Composite Laboratory, Aerospace Research Institute of Materials and Processing Technology, Beijing 100076, China.
Polymers (Basel). 2022 Aug 19;14(16):3390. doi: 10.3390/polym14163390.
Surface defects of fiber-reinforced resin matrix composites (FRRMCs) adversely affect their appearance and performance. To accurately and efficiently detect the three-dimensional (3D) surface defects of FRRMCs, a novel lightweight and two-stage semantic segmentation network, i.e., Mask-Point, is proposed. Stage 1 of Mask-Point is the multi-head 3D region proposal extractors (RPEs), generating several 3D regions of interest (ROIs). Stage 2 is the 3D aggregation stage composed of the shared classifier, shared filter, and non-maximum suppression (NMS). The two stages work together to detect the surface defects. To evaluate the performance of Mask-Point, a new 3D surface defects dataset of FRRMCs containing about 120 million points is produced. Training and test experiments show that the accuracy and the mean intersection of union (mIoU) increase as the number of different 3D RPEs increases in Stage 1, but the inference speed becomes slower when the number of different 3D RPEs increases. The best accuracy, mIoU, and inference speed of the Mask-Point model could reach 0.9997, 0.9402, and 320,000 points/s, respectively. Moreover, comparison experiments also show that Mask-Point offers relatively the best segmentation performance compared with several other typical 3D semantic segmentation networks. The mIoU of Mask-Point is about 30% ahead of the sub-optimal 3D semantic segmentation network PointNet. In addition, a distributed surface defects detection system based on Mask-Point is developed. The system is applied to scan real FRRMC products and detect their surface defects, and it achieves the relatively best detection performance in competition with skilled human workers. The above experiments demonstrate that the proposed Mask-Point could accurately and efficiently detect 3D surface defects of FRRMCs, and the Mask-Point also provides a new potential solution for the 3D surface defects detection of other similar materials.
纤维增强树脂基复合材料（FRRMC）的表面缺陷会对其外观和性能产生不利影响。为了准确、高效地检测FRRMC的三维（3D）表面缺陷，提出了一种新型的轻量级两阶段语义分割网络，即Mask-Point。Mask-Point的第一阶段是多头3D区域提议提取器（RPE），用于生成多个3D感兴趣区域（ROI）。第二阶段是由共享分类器、共享滤波器和非极大值抑制（NMS）组成的3D聚合阶段。这两个阶段协同工作以检测表面缺陷。为了评估Mask-Point的性能，生成了一个新的包含约1.2亿个点的FRRMC的3D表面缺陷数据集。训练和测试实验表明，随着第一阶段中不同3D RPE数量的增加，准确率和平均交并比（mIoU）会提高，但当不同3D RPE数量增加时，推理速度会变慢。Mask-Point模型的最佳准确率、mIoU和推理速度分别可达0.9997、0.9402和320000点/秒。此外，对比实验还表明，与其他几个典型的3D语义分割网络相比，Mask-Point具有相对最佳的分割性能。Mask-Point的mIoU比次优的3D语义分割网络PointNet领先约30%。此外，还开发了基于Mask-Point的分布式表面缺陷检测系统。该系统应用于扫描实际的FRRMC产品并检测其表面缺陷，在与熟练工人的竞争中实现了相对最佳的检测性能。上述实验表明，所提出的Mask-Point能够准确、高效地检测FRRMC的3D表面缺陷，并且Mask-Point还为其他类似材料的3D表面缺陷检测提供了一种新的潜在解决方案。
