  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.
