根据前期工作存在的诸多问题，本文设计了一套图像采集系统，该系统设计开发基于 Cypress公司的 EZ-USB芯片CYUSB3065的USB3.0外设解决方案。该系统采用CMOS图像传感器IMX335LQN-C进行图像采集，并利用USB3.0传输方式实现了CMOS图像传感器与PC的高速数据传输。该系统在结构上具有体积小，便捷性高等特点，在采集性能上具有可靠性好，图像质量高等特点。此外，本文基于该设备构建包含各类缺陷类型的不透明空瓶内壁缺陷检测数据集。
With the improvement of living standards and consumption capacity, people buy more and more liquid goods, such as beverages, dairy products, wine, etc., which greatly stimulates the production of enterprises. With the growth of the output of liquid commodities, the demand for bottles is also growing rapidly year by year. At the same time, consumers pay high attention to food safety. As for liquid commodity manufacturers, they should shoulder the main responsibility to detect the defects of empty bottles before filling liquid commodities, so as to provide high-quality products that meet the industry standard operating procedures. At present, the defect detection of empty bottles at home and abroad mostly focuses on the defect detection of the bottom, mouth and body of transparent bottles and the appearance of opaque bottles. However, due to the difficulty of internal detection of opaque empty bottles, there are few defect detection algorithms and machines for the inner wall of opaque empty bottles.
Based on this background, the thesis designs a set of defect detection device for the inner wall of the opaque empty bottle and constructs the corresponding defect data set on the basis of the previous work in the group. Aiming at this scenario, the thesis proposes a method for detecting the defects on the inner wall of the opaque empty bottle based on data augmentation, and includes the research on the lightweight of the detection model. The main research contents of the thesis are as follows:
(1) Distributed image fast acquisition system based on USB3.0
According to many problems existing in the previous work, the thesis designs an image acquisition system, which is based on the USB3.0 peripheral solution of Cypress's EZ-USB chip CYUSB3065. The system uses CMOS image sensor IMX335LQN-C for image acquisition, and uses USB3.0 transmission mode to realize high-speed data transmission between CMOS image sensor and PC. The system has the characteristics of small size, high convenience, good reliability and high image quality in terms of acquisition performance. In addition, based on the equipment, the thesis constructs a data set for the detection of the defects on the inner wall of the opaque empty bottle. The data set contains various types of defects.
(2) YOLOv5 detection model for defects on the inner wall of the opaque empty bottle based on data augmentation
Affected by the imbalance of training data, defect detection models often have problems such as poor robustness. To further improve the performance of defect detection model, the thesis proposes a YOLOv5 defect detection model based on recursive data augmentation framework. The framework can flexibly adjust the composition of defect block source library and data balance strategy according to the characteristics of existing data, so as to improve the defect detection capability of the model without changing the structure of YOLOv5 model. Abundant frame comparison experiments and model performance comparison experiments have verified the good performance of YOLOv5 model based on the data augmentation framework on the data set for the detection of the defects on the inner wall of the opaque empty bottle.
(3) Research on lightweight method for detecting defects on the inner wall of the opaque empty bottle
In the actual production process, the defect detection model often has problems such as high computing power requirements and poor real-time performance. These problems will cause the large-scale model with high performance to be unable to be deployed effectively, but at the same time, the model with too small parameters has a bottleneck in performance and cannot achieve accurate defect detection. To solve these problems, the thesis constructs a lightweight cooperative detection model for the detection of defects on the inner wall of the opaque empty bottle. The cooperation model includes a pre-trained high-performance teacher model and an efficient student model finely tuned by knowledge distillation. A wealth of quantitative and qualitative comparative experiments show that the cooperation model can achieve an efficient balance between recognition performance and recognition speed, and has good detection performance on the defect data set in the thesis.
|Keyword||缺陷检测 图像采集系统 YOLOv5 数据增广 模型轻量化|
|Sub direction classification||其他|
|planning direction of the national heavy laboratory||其他|
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