# Tire Bubble Defect Detection Using Incremental Learning
## 1 Introduction  
Industry 4.0 enables data communication, collection and analysis by machines, enabling faster, more agile and efficient processes to manufacture quality products with minimal expenditure. Since 2012, researches on computer vision tasks based on convolutional neural networks have progressed fairly rapidly [1, 2].  
During the tire manufacturing process, air may be trapped in the tire to form bubbles. The bubbles can compromise the tire's structural integrity, leading to the risk of a punctured tire if the pressure is not distributed evenly. Therefore, tire manufacturers inspect the tires for bubble defects before they are sold to reduce the risk of punctures. The tire bubbles are difficult to detect by the naked eye since they are internal defects. The operator puts the tire into the machine, and air bubbles in the tire cause the tire surface to bulge in the vacuum. Digital shearography [3] is used to obtain speckle images of the tire at different angles for manual inspections. However, tired or inexperienced inspectors can lead to inaccuracies and inconsistency in the inspection outcomes.  
The tire defect detection process is extremely labor and time-intensive, even though it is only a small part of the quality control process in the tire manufacturing industry. Many difficulties arise during the inspection due to the inconsistent and subjective assessments by inspectors with different experiences. To ensure product quality and reduce labor costs, deep learning has been gradually incorporated in the industry by many manufacturers to reduce labor-intensive work and improve production efficiency effectively. With the help of artificial intelligence, operators only need to confirm the bubble defects detected by machine, which can reduce the workload of labors. Manufacturers do not need to recruit alot of labor to detect bubble defects one by one; developments in automation technology can also address the problems caused by inconsistent inspection standards.  
In our previous research [4; 5; 6], we divided the entire speckle image into several blocks, and a convolutional neural network is used to detect the tread bubbles and the sidewall separately. However, there is still room for improvement regarding the detection speed and detection rate. In this paper, we use an object detection method to improve the bubble defect detection speed and detection rate. In addition, to address the situation where the model developed in the laboratory is actually introduced into the field with a reduced defect detection rate, we propose an incremental YOLO (You Only Look Once) architecture to add on-site misidentified samples to model training to improve the model's flaw detection accuracy on the actual site. The YOLO machine learning algorithm uses features learned by a deep convolutional neural network to detect objects in real time. YOLO has the advantage of being much faster than other networks and still maintaining high accuracy. Experimental results show that the improved YOLOv3 SPP outperforms other YOLO series architectures (Tiny YOLO, YOLOv3 and YOLOv3 SPP) in bubble defect detection [7; 8; 9; 10]. Furthermore, the proposed incremental learning also demonstrates the effectiveness of improving the detection accuracy of bubble defects in real field.  
The rest of this paper is organized as follows. First, the proposed method is described in detail in Section 2. Then, we present the experimental results and discuss the results in Section 3 and, finally, the conclusions are delivered in Section 4.