An Industrial Automated Defect Detection Method For Wire Packaging Based On Deep Learning

Defect detection is not only the key link of product quality control in industrial production,but also the bottleneck of realizing full automation in industrial production.At present,the artificial product defect detection method has some disadvantages,such as low detection efficiency,high cost and easy false detection,which can not meet the production requirements of high-speed automation in industry.And the defect detection method that based on traditional machine vision,to some extent,although can make up for the shortcoming of artificial detection,its detection equipment is often complicated in structure and expensive in price,and can only be applied to the detection of certain goals.When the detection target changed,a person with professional knowledge is needed to complex the feature extraction and feature selection process again.Due to the fact that the characteristics of the artificial design can not effectively cover the all features that make one defect,so this defect detection method is limited in efficiency and accuracy.At the same time in the complex industrial environment and complex detection background of the application scene,the traditional machine vision detection methods are also limited.This paper studies the development status of defect detection,In view of the problems faced in current industrial defect detection field,an industrial automated defect detection method based on the combination of Inception-V3 image classification algorithm and YOLO-V3 target detection algorithm is proposed under the condition of few sample data,which avoids the complex feature extraction process of traditional machine vision defect detection algorithm: First,the image to be tested is put into the YOLO-V3 target detection model to obtain the "areas of interest",and then these areas are successively enhanced with data augmentation,and then the Inception-V3 defect recognition model is adopted to obtain the final defect recognition result by taking the average value of the obtained recognition results and comparing with the set threshold value.In this paper,we trained the YOLO-V3 target detection model and Inception-V3 defect recognition model respectively through collecting pictures,making the experiment data sets,using data augmentation and transfer learning technology.Then,the validity of the defect detection method proposed in this paper is verified through experiments.Compared with the detection method that only applies image classification algorithm,the detection method has higher accuracy and stability.Meanwhile,the method can be applied in complex detection environment and detection background,showing advantages in multi-object detection.In this paper,a complete set of modular defect detection system is designed for the proposed defect detection method,including data acquisition module,image detection module,image recognition module,alarm module and data storage module.Communication and data sharing between modules is realized through Modbus TCP protocol and the publish and subscribe mechanism of MQTT and Redis.The recognition model is deployed via Tensor Flow Serving which supports g RPC invocation and hot deployment to facilitate model management.Not only the hardware cost is low,but also the operation of deployment and expansion is simple of this system and can be applied to large-scale industrial automation application scenarios.It is of great significance to small and medium-sized enterprises to realize automated defect detection and improve their production efficiency.