Surface defect detection plays an essential role in industry, and it is challenging due to the following problems: 1) the similarity between defect and nondefect texture is very high, which eventually leads to recognition or classification errors and 2) the size of defects is tiny, which are much more difficult to be detected than larger ones. To address such problems, this article proposes an adaptive image segmentation network (AIS-Net) for pixelwise segmentation of surface defects. It consists of three main parts: multishuffle-block dilated convolution (MSDC), dual attention context guidance (DACG), and adaptive category prediction (ACP) modules, where MSDC is designed to merge the multiscale defect features for avoiding the loss of tiny defect feature caused by model depth, DACG is designed to capture more contextual information from the defect feature map for locating defect regions and obtaining clear segmentation boundaries, and ACP is used to make classification and regression for predicting defect categories. Experimental results show that the proposed AIS-Net is superior to the state-of-the-art approaches on four actual surface defect datasets (NEU-DET: 98.38% $\pm$ 0.03%, DAGM: 99.25% $\pm$ 0.02%, Magnetic-tile: 98.73% $\pm$ 0.13%, and MVTec: 99.72% $\pm$ 0.02%).

Automatic diagnosis of COVID-19 using chest CT images is of great significance for preventing its spread. However, it is difficult to precisely identify COVID-19 due to the following problems: 1) the location and size of lesions can vary greatly in CT images; 2) its unique characteristics are often imperceptible in imaging findings. To solve these problems, a Deep Dual Attention Network ( $\textrm {D}^{\textrm {2}}\textrm {ANet}$ ) is proposed for accurate diagnosis of COVID-19 by integrating dual attention modules (DAMs) with different scales of the feature extractor, where DAM can adaptively detect relevant lesion regions to extract discriminative imaging features of COVID-19. Specifically, DAM is implemented by two parallel blocks: global attention block (GAB) and local attention block (LAB), in which GAB is designed to roughly locate the infected regions from the entire image by modeling global contexts, while LAB is developed to explicitly highlight subtle differences of COVID-19 from other viral pneumonia in the infected regions by learning detailed lesion information. Experimental results on several public datasets show that $\textrm {D}^{\textrm {2}}\textrm {ANet}$ outperforms the state-of-the-art approaches in various performance metrics.

Surface defect detection plays a vital role in manufacturing. However, it remains challenging: 1) small samples due to fewer defective products relative to numerous normal products and 2) small defects due to the small size of the defect relative to the large surface. In order to address these problems, this article proposes a triple attention semantic segmentation network for pixel-wise segmentation of surface defects, named as TAS ² -Net. In the TAS ² -Net framework, for solving the issue of small samples, a generative adversarial network (GAN)-based data augmentation method is proposed to generate defect training samples from a limited number of true defects. Furthermore, for avoiding the loss of small defects, a multilevel features extraction (MFE) module is proposed to extract multi-level features of small defects. Then, a triple context attention module is designed to capture more defect context information for better identifying these small defects. Finally, the focus context module (FCM) is utilized to fuse defect information and achieve pixel-wise segmentation on small defects. Experiments on three actual defect datasets show that the proposed TAS ² -Net outperforms the state-of-the-art methods in terms of precision, recall, ${F}$ -measure, and Intersection over Union (IoU) (DAGM: 86.9%, NEU-DET: 86.3%, Magnetic-Tile: 85.1%).

Printed circuit board (PCB) defect detection is a part of the quality control process, which detects and identifies predamage in finished products. However, it is difficult to detect them due to small defects. To this end, this article designs a refined defect detector (RDTor) with deformable transformer and pyramid feature fusion to precisely capture small defects for achieving the defect detection of PCB, where RDTor is comprised of three parts: multihead nonlocal transformer (MNT) module, multiscale pyramid feature fusion (MPFF) module, and adaptive defect detection (ADD) ones. Specifically, an MNT module is first developed to adaptively focus on the defect areas for highlighting defect features and suppressing nondefect background ones, an MPFF module is proposed to preserve the features of small defects much more as network deepening, and an ADD module is presented to adaptively perform the defect inspection for obtaining defect categories and defect prediction box. The experimental results on a large-scale PCB image dataset acquired from real-world industrial products show that the proposed method achieves the state-of-the-art accuracy in industrial applications, where it can achieve 99.6% (accuracy), 97.2% (precision), 98.8% (recall), 98.0% (F1-score), and 99.3% mean Average Precision (mAP) in terms of multiscale defects classification and detection results.

The noninvasive fetal ECG (FECG) is used to monitor fetal well-being at prenatal and intrapartum. However, it is challenging to detect the FECG signal from the abdominal electrocardiogram (ECG) due to the following problems: 1) the FECG signal is weak and often masked by noise and 2) the FECG signal is mixed or overlapped with the maternal ECG (MECG) signal. To solve such problems, a period-aware attention network (PA ² Net) is proposed for FECG detection by designing three modules, where an FECG period-aware attention module (FPAM), which is designed to suppress the noise interference by modeling the periods and features of signals, is first employed to detect FECG signals masked in noise; next, an MECG period-aware attention module, which is generated from the FPAM by the KL-divergence-based weight sharing module, can collaborate with the FPAM to search for mixed ECG signals of FECG and MECG. Finally, an antialiasing signal separation module is developed to estimate FECG signals from mixed ECG signals. Experiments conducted on the benchmarks show that the proposed PA ² Net can achieve excellent performance with the PPV of 98.83%, 99.74%, 99.67%, and 98.47% on PCDB, ADFECGDB, NIFECGDB, and LHDB databases, respectively, allowing obstetricians to use PA ² Net to monitor fetal health and diagnose diseases.