  Defect detection is a basic and essential task in automatic parts production,
especially for automotive engine precision parts. In this paper, we propose a
new idea to construct a deep convolutional network combining related knowledge
of feature processing and the representation ability of deep learning. Our
algorithm consists of a pixel-wise segmentation Deep Neural Network (DNN) and a
feature refining network. The fully convolutional DNN is presented to learn
basic features of parts defects. After that, several typical traditional
methods which are used to refine the segmentation results are transformed into
convolutional manners and integrated. We assemble these methods as a shallow
network with fixed weights and empirical thresholds. These thresholds are then
released to enhance its adaptation ability and realize end-to-end training.
Testing results on different datasets show that the proposed method has good
portability and outperforms the state-of-the-art algorithms.
