Automated defect inspection has long been a challenging task especially in industrial applications, where collecting and labeling large amounts of defective samples are usually harsh and impracticable. In this paper, we propose an approach to detect and localize defects with only defect-free samples for model training. This approach is carried out by reconstructing image patches with convolutional denoising autoencoder networks at different Gaussian pyramid levels, and synthesizing detection results from these different resolution channels. Reconstruction residuals of the training patches are used as the indicator for direct pixelwise defect prediction, and the reconstruction residual map generated in each channel is combined to generate the final inspection result. This novel method has two prominent characteristics, which benefit the implementation of automatic defect inspection in practice. First, it is absolutely unsupervised that no human intervention is needed throughout the inspection process. Second, multimodal strategy is utilized in this method to synthesize results from multiple pyramid levels. This strategy is capable of improving the robustness and accuracy of the method. To evaluate this approach, experiments on convergence, noise immunity, and defect inspection accuracy are conducted. Furthermore, comparative tests with some excellent algorithms on actual and simulated data sets are performed. Experimental results demonstrated the effectiveness and superiority of the proposed method on homogeneous and nonregular textured surfaces.

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 1920x 1080 pixels. The extensive experimental results demonstrate that MS-FCAE achieves highly efficient and state-of-the-art inspection accuracy.

Flat panel displays, such as the thin film transistor liquid crystal display (TFT-LCD), the organic light-emitting diode (OLED) and the polymer light-emitting diode (PLED), have been widely applied in many fields in recent decades. To ensure the quality of these displays, defect inspection is crucial. Mura defects, which are phenomena of uneven screen displays, are the most challenging visual defects to detect. This paper presents an online sequential classifier and transfer learning (OSC-TL) method for the online training and classification of Mura defects. OSC-TL is a new method that combines a deep convolutional feature extractor and a sequential extreme learning machine classifier. It makes online sequential training in a production line possible. To demonstrate the performance of the OSC-TL method, several experiments are performed to compare the results of this method with those of other popular classification algorithms. The experimental results show that the computational resources and time consumed by OSC-TL are well below those of other common methods because of the feature transfer and the online sequential classification strategies. Consequently, the OSC-TL method has been implemented in our automated optical inspection equipment to perform online Mura defect classification. It is able to learn and recognize a Mura defect image within 1.5 milliseconds.

Establishing a unified model for defect inspection remains a challenge in the industrial automation field due to the various texture surfaces. Current unsupervised methods cannot address anomalies, which causes these methods cannot inspect various texture defects. In this paper, we propose a novel unsupervised anomaly-feature-editing-based adversarial network (AFEAN) for accurate inspection of various texture defects. First, a paired input, consisting of defect-free image and its artificially defective image, is utilized to train AFEAN. Subsequently, a novel anomaly feature detection module is proposed to detect anomaly features in the latent space. Next, a global context feature editing module is proposed to suppress the reconstruction of defects. Finally, a feature decoding module utilizes the edited features to reconstruct the texture background. In the testing phase, the defects are segmented by the residuals between the input and the reconstructed texture background. The extensive experimental results demonstrate that AFEAN achieves state-of-the-art inspection accuracy.

Object detection remains a challenging task in computer vision due to the tremendous extent of changes in the appearances of objects caused by clustered backgrounds, occlusion, truncation and scale change. Current deep neural network (DNN)-based object detection methods cannot simultaneously achieve a high accuracy and a high efficiency. To overcome this limitation, in this study, we propose a novel multi-scale attention deep neural network (MSA-DNN) for accurate object detection with high efficiency. The proposed MSA-DNN method utilizes a novel multi-scale feature fusion module (MSFFM) to construct high-level semantic features. Subsequently, a novel multi-scale attention module (MSAM) based on the fused layers of the MSFFM is introduced to exploit the global semantic information of image-level labels to guide detection. On the one hand, MSAM can capture global semantic information to further enhance the semantic feature representation of the fused layers constructed by the MSFFM, thereby improving the detection accuracy. On the other hand, the multi-scale attention maps generated by MSAM can be employed to rapidly and coarsely locate objects at different scales. In addition, an attention-based hard negative mining strategy (AHNM) is introduced to filter out negative samples to reduce the search space, dramatically alleviating the severe class imbalance problem. Extensive experimental results on the challenging PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO datasets demonstrate that MSA-DNN achieves a stateof- the-art detection accuracy while maintaining a high efficiency. Furthermore, MSA-DNN significantly improves the small-object detection accuracy.