Decision Fusion Network with Perception Fine-tuning for Defect Classification

Surface defect inspection is an important task in industrial inspection. Deep learning-based methods have demonstrated promising performance in this domain. Nevertheless, these methods still suffer from misjudgment when encountering challenges such as low-contrast defects and complex backgrounds. To overcome these issues, we present a decision fusion network (DFNet) that incorporates the semantic decision with the feature decision to strengthen the decision ability of the network. In particular, we introduce a decision fusion module (DFM) that extracts a semantic vector from the semantic decision branch and a feature vector for the feature decision branch and fuses them to make the final classification decision. In addition, we propose a perception fine-tuning module (PFM) that fine-tunes the foreground and background during the segmentation stage. PFM generates the semantic and feature outputs that are sent to the classification decision stage. Furthermore, we present an inner-outer separation weight matrix to address the impact of label edge uncertainty during segmentation supervision. Our experimental results on the publicly available datasets including KolektorSDD2 (96.1% AP) and Magnetic-tile-defect-datasets (94.6% mAP) demonstrate the effectiveness of the proposed method

Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects

Surface defect inspection is a very challenging task in which surface defects usually show weak appearances or exist under complex backgrounds. Most high-accuracy defect detection methods require expensive computation and storage overhead, making them less practical in some resource-constrained defect detection applications. Although some lightweight methods have achieved real-time inference speed with fewer parameters, they show poor detection accuracy in complex defect scenarios. To this end, we develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depth-wise Self-Attention (DSA) module. The proposed DSA performs element-wise similarity in channel dimension while maintaining linear complexity. In addition, we introduce a novel Channel Reference Attention (CRA) module before each decoder block to strengthen the representation of multi-level features in the bottom-up path. The proposed CRA exploits the channel correlation between features at different layers to adaptively enhance feature representation. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods. Specifically, GCANet achieves competitive accuracy (91.79% $F_{\beta}^{w}$, 93.55% $S_\alpha$, and 97.35% $E_\phi$) on SD-saliency-900 while running 272fps on a single gpu

Scanning Tunneling Microscopic Observation of the Atomic Structure of GaAs(001) Surface Grown by Metalorganic Vapor Phase Epitaxy(STM-GaAs)

We present the first atomically resolved scanning-tunneling micrographs of GaAs(001) surfaces prepared by metalorganic vapor-phase epitaxy (MOVPE). Thin films deposited in an MOVPE reactor were transferred to an ultra high vacuum system without air exposure. After heating the samples from 450 to 620℃, high-quality images of the (2x4)/c(2x8), (1x6)/(2x6) and (4x2)/c(8x2) reconstructions were obtained

Bacterial effector restricts liquid-liquid phase separation of ZPR1 to antagonize host UPRER

Summary: How pathogens manipulate host UPRER to mediate immune evasion is largely unknown. Here, we identify the host zinc finger protein ZPR1 as an interacting partner of the enteropathogenic E. coli (EPEC) effector NleE using proximity-enabled protein crosslinking. We show that ZPR1 assembles via liquid-liquid phase separation (LLPS) in vitro and regulates CHOP-mediated UPRER at the transcriptional level. Interestingly, in vitro studies show that the ZPR1 binding ability with K63-ubiquitin chains, which promotes LLPS of ZPR1, is disrupted by NleE. Further analyses indicate that EPEC restricts host UPRER pathways at the transcription level in a NleE-ZPR1 cascade-dependent manner. Together, our study reveals the mechanism by which EPEC interferes with CHOP-UPRER via regulating ZPR1 to help pathogens escape host defense