论文标题
SSD-FASTER NET：工业缺陷检查的混合网络
SSD-Faster Net: A Hybrid Network for Industrial Defect Inspection
论文作者
论文摘要
工业组件的质量对于机器人等特殊设备的生产至关重要。这些组件的缺陷检查是确保质量的有效方法。在本文中，我们提出了一个混合网络，即SSD-Faster网络，用于对铁轨，绝缘体，换向器等的工业缺陷检查。SSD-FASTER NET是一个两阶段的网络，包括用于快速定位有缺陷块的SSD，以及一个不断干扰的更快的R-CNN，用于缺陷。对于前者，我们提出了一种新颖的切片定位机制，以快速帮助SSD扫描。第二阶段是基于使用FPN，可变形的内核（DK）改进的更快的R-CNN来增强表示能力。它融合了多尺度信息，并自给自足。我们还提出了一种新颖的损失功能，并使用ROI对齐来提高准确性。实验表明，基于更快的R-CNN，我们的SSD速度网的平均准确性为84.03％，比最近的竞争对手高13.42％，比基于GAN的方法高4.14％，比基于DNN的检测器高10％以上。并且计算速度提高了近7％，这证明了其稳健性和出色的性能。
The quality of industrial components is critical to the production of special equipment such as robots. Defect inspection of these components is an efficient way to ensure quality. In this paper, we propose a hybrid network, SSD-Faster Net, for industrial defect inspection of rails, insulators, commutators etc. SSD-Faster Net is a two-stage network, including SSD for quickly locating defective blocks, and an improved Faster R-CNN for defect segmentation. For the former, we propose a novel slice localization mechanism to help SSD scan quickly. The second stage is based on improved Faster R-CNN, using FPN, deformable kernel(DK) to enhance representation ability. It fuses multi-scale information, and self-adapts the receptive field. We also propose a novel loss function and use ROI Align to improve accuracy. Experiments show that our SSD-Faster Net achieves an average accuracy of 84.03%, which is 13.42% higher than the nearest competitor based on Faster R-CNN, 4.14% better than GAN-based methods, more than 10% higher than that of DNN-based detectors. And the computing speed is improved by nearly 7%, which proves its robustness and superior performance.
