Abstract
This paper aims to achieve real-time and accurate detection of surface defects by using a deep learning method. For this purpose, the Single Shot MultiBox Detector (SSD) network was adopted as the meta structure and combined with the base convolution neural network (CNN) MobileNet into the MobileNet-SSD. Then, a detection method for surface defects was proposed based on the MobileNet-SSD. Specifically, the structure of the SSD was optimized without sacrificing its accuracy, and the network structure and parameters were adjusted to streamline the detection model. The proposed method was applied to the detection of typical defects like breaches, dents, burrs and abrasions on the sealing surface of a container in the filling line. The results show that our method can automatically detect surface defects more accurately and rapidly than lightweight network methods and traditional machine learning methods. The research results shed new light on defect detection in actual industrial scenarios.
Highlights
Intellisense and pattern recognition technologies have made progress in robotics [1,2,3], computer engineering [4,5], health-related issues [6], natural sciences [7] and industrial academic areas [8,9].Among them, computer vision technology develops quickly
This paper presents a surface defect detection method based on MobileNet-Single Shot MultiBox Detector (SSD)
Conv_BN_ReLU6 ismodel a standard convolutional layer, while Conv1_Dw_Pw as the training base net of the pre-processed database; the trained was migrated to the ensure the imaging of the acquisition unit
Summary
It mainly uses a binary camera, digital camera, depth camera and charge-coupled device (CCD) camera to collect target images, extract features and establish corresponding mathematical models, and to complete the processing of target recognition, tracking and measurement. After contour extraction of the acquired depth image data, the Hidden Markov Model (HMM) is used to identify human activity. This system is highly accurate in recognition and has the ability to effectively deal with rotation and deficiency of the body [10]. In [12], vehicle damage is classified by a deep learning method, and the recognition accuracy of a small data set was up to 89.5% by the introduction of transfer learning and an integrated learning method
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