# Wheel Hub Defects Image Recognition Base on Zero-Shot Learning
## 1 Instruction  
The automobile is an indispensable means of transportation in people's daily life, it is also an important part of the national economy, and the wheel hub is one of the key parts of the automobile. With the development of China's automobile industry, the technical level of wheel hub manufacturing enterprises is constantly improving, and product export volume is large and continues to grow. However, due to the rapid growth of production and the imperfection of processing technology, more than 40 defects have been found in wheel hub products. These defects not only affect the good appearance and brand image of products but also lead to serious traffic accidents. Therefore, researchers have done some work, such as [PERSON] [1], in order to improve the accuracy of automobile wheel hub defect image detection and recognition, an improved peak algorithm--the trend peak algorithm--was proposed to extract the wheel hub defect area and combined with the BP neural network to recognize the wheel hub defects. [PERSON] [2] aimed at the internal defects such as air hole and shrinkage cavity in the process of low-pressure casting of the wheel hub, a method of defect extraction based on mathematical morphology was employed, all of which belongs to the traditional recognition method's artificial design feature, so in the face of complex samples, the robustness is poor. While deep learning technology has advantages: automatic feature extraction, weight sharing, and needless image preprocessing. For example, [PERSON] [3] used Faster R-CNN with ResNet-101 as the target detection algorithm to detect scratches and points on the wheel hub. [PERSON] [4] put forward an improved Faster R-CNN recognition model for multiple types of wheel hub defects, that is, by improving the shared network ZFNet to two revised branches (RPN and Fast R-CNN), and then four typical wheel hub defects are identified. Although the methods based on deep learning have made some progress, we find that existing supervised learning strategies rely on a large amount of labeled data. However, some defect samples are scarce or none at all, so they cannot meetthe requirements of the deep network training, which leads to the performance breakdown. At present, there are some different solutions, such as transfer learning [5], self-taught learning [6], and few-shot learning [7]. However, these methods are unable to cope with the condition of zero samples. When the test samples are never seen during training, which is called zero-shot learning (ZSL) [8], as shown in Figure 1.  
As we know, humans have the ability to retain knowledge from past learning tasks and use that knowledge to quickly integrate and solve new recognition tasks. Specifically, humans can easily identify these rare categories by using semantic description and their relationship to the seen classes. For example, a person could identify a new species \"pen-quin\" by the semantic description, \"a flightless bird living in the Antarctic\", or \"kangaroo\" by the portrayal, \"an Australian animal with a pouch on its body\", inspired by which we try to empower machines with the same intelligence, in the process of achieving this goal, zero-shot learning is essential to the realization of machine intelligence. ZSL has broad prospects in the fields of autonomous driving [9], medical imaging analysis [10], intelligent manufacturing [11], robotics [12], etc. In these fields, although it is difficult to obtain new labeled images, advanced semantic descriptions of categories can be easily obtained. In order to identify unseen classes, usually, a large scale of labeled samples (seen classes) is needed to train the deep model, and then the model is adapted to an unseen one. For ZSL, the seen and unseen classes are associated through a high-dimensional vector space of semantic descriptors. Each class corresponds to a unique semantic descriptor. Semantic descriptors can take the form of manually defined attributes [13] or automatically extracted word vectors [14].