Multiscale Feature-Clustering-Based Fully Convolutional Autoencoder for Fast Accurate Visual Inspection of Texture Surface Defects

Visual inspection of texture surface defects is still a challenging task in the industrial automation field due to the tremendous changes in the appearance of various surface textures. Current visual inspection methods cannot simultaneously and efficiently inspect various types of texture defects due to either the low discriminative capabilities of handcrafted features or their time-consuming sliding-window strategy. In this paper, we present a novel unsupervised multiscale feature-clustering-based fully convolutional autoencoder (MS-FCAE) method that efficiently and accurately inspects various types of texture defects based on a small number of defect-free texture samples. The proposed MS-FCAE method utilizes multiple FCAE subnetworks at different scale levels to reconstruct several textured background images. The residual images are obtained by subtracting these texture backgrounds from the input image individually; then, they are fused into one defect image. To maximize the efficiency, each FCAE subnetwork utilizes fully convolutional neural networks to extract the original feature maps directly from the input images. Meanwhile, each FCAE subnetwork performs feature clustering to improve the discriminant power of the encoded feature maps. The proposed MS-FCAE method is evaluated on several texture surface inspection data sets both qualitatively and quantitatively. This method achieves a Precision of 92.0% while requiring only 82 ms for input images of $1920\times 1080$ pixels. The extensive experimental results demonstrate that MS-FCAE achieves highly efficient and state-of-the-art inspection accuracy. Note to Practitioners —Most conventional visual inspection methods can address only one specific type of texture defect, while multiscale feature-clustering-based fully convolutional autoencoder (MS-FCAE) can simultaneously and accurately inspect various types of texture surface defects, such as those of thin-film transistor liquid crystal displays, wood, fabrics, and ceramic tiles. Furthermore, MS-FCAE requires only a small number of surface texture samples to learn a robust network model, and its training requires no defect samples. This is extremely important for industrial applications because identifying and labeling defect samples is difficult. Moreover, MS-FCAE can be applied to online visual inspection utilizing a graphics processing unit-based parallel processing strategy.