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Open AccessArticle

A Compact Convolutional Neural Network for Surface Defect Inspection

Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing 100190, China
Civil Engineering & Engineering Mechanics Department, Columbia University, New York, NY 10024, USA
Author to whom correspondence should be addressed.
Current address: No. 95 Zhongguancun East Road, Beijing 100190, China.
Sensors 2020, 20(7), 1974;
Received: 9 February 2020 / Revised: 29 March 2020 / Accepted: 29 March 2020 / Published: 1 April 2020
(This article belongs to the Section Intelligent Sensors)
The advent of convolutional neural networks (CNNs) has accelerated the progress of computer vision from many aspects. However, the majority of the existing CNNs heavily rely on expensive GPUs (graphics processing units). to support large computations. Therefore, CNNs have not been widely used to inspect surface defects in the manufacturing field yet. In this paper, we develop a compact CNN-based model that not only achieves high performance on tiny defect inspection but can be run on low-frequency CPUs (central processing units). Our model consists of a light-weight (LW) bottleneck and a decoder. By a pyramid of lightweight kernels, the LW bottleneck provides rich features with less computational cost. The decoder is also built in a lightweight way, which consists of an atrous spatial pyramid pooling (ASPP) and depthwise separable convolution layers. These lightweight designs reduce the redundant weights and computation greatly. We train our models on groups of surface datasets. The model can successfully classify/segment surface defects with an Intel i3-4010U CPU within 30 ms. Our model obtains similar accuracy with MobileNetV2 while only has less than its 1/3 FLOPs (floating-point operations per second) and 1/8 weights. Our experiments indicate CNNs can be compact and hardware-friendly for future applications in the automated surface inspection (ASI). View Full-Text
Keywords: surface defect inspection; convolutional neural network; machine vision surface defect inspection; convolutional neural network; machine vision
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Huang, Y.; Qiu, C.; Wang, X.; Wang, S.; Yuan, K. A Compact Convolutional Neural Network for Surface Defect Inspection. Sensors 2020, 20, 1974.

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