The defect on the fabric surface is one of the important factors affecting the quality of fabrics. Defect detection becomes the core means of quality control. Current deep-Iearning-based defect detection methods present a significant challenge in detection accuracy due to the diversity of fabric patterns and the scarcity of defect samples. Inspired by the transfer-learning paradigm, this article proposes a novel YOLOv7-based fine-tuning method for fabric defect detection. Specifically, we first employ a well-trained object detection network (i.e. YOLOv7) as a benchmark model to locate and classify defect-picking points. Considering the fabrics are mostly occluded and shaded, we therefore introduce a multi-channel attention mechanism and design the corresponding loss function. Finally, we manually clean a fabric defect dataset and use it to fine-tune the YOLOv7. Extensive experiments tested on public datasets show that our fine-tuned YOLOv7 improves mAP by 3.5%, precision by 6.3%, and recall rate by 5.7% compared to the baseline model YOLOv7. Especially, our fine-tuning method leads to a 6 MB reduction in model size. Further ablation studies demonstrate the effectiveness and contribution of our designs toward the whole network model.

Railway brake shoe defects pose substantial risks to railway operational safety because subtle micro-cracks, edge wear, and surface-adhered slag may be difficult to distinguish from rust, shadows, oil stains, and other industrial noise. To address this challenge, we propose Adapter-enhanced CLIP (ACLIP), a parameter-efficient vision-language anomaly detection framework for railway brake shoe inspection. ACLIP inserts lightweight residual adapters into the late transformer layers of a frozen CLIP ViT-L/14 backbone, preserving the general vision-language knowledge of CLIP while adapting it to fine-grained industrial defect patterns. A two-stage training strategy is introduced. Stage 1 disentangles normal and anomalous text anchors, and Stage 2 aligns local visual patches with the refined semantic anchors using image-level labels only. In this work, “annotation-free” refers strictly to the training stage: ACLIP does not use pixel-level masks for optimization. The generated heatmaps are therefore treated as defect-indicative visual explanations on the private brake shoe dataset, while quantitative localization evaluation is reported only on public datasets where pixel-level masks are available for testing. Experiments are conducted on a real-world railway brake shoe dataset under stratified five-fold cross-validation and on two public industrial anomaly detection benchmarks, MVTec AD and VisA. ACLIP achieves 99.28% classification accuracy and 99.98% image-level AUROC on the brake shoe dataset and maintains 85.00% image-level AUROC in the 2-shot setting. Public benchmark evaluation reports image-level AUROC, pixel-level AUROC, and AUPRO to assess both detection and heatmap-based localization behavior. The results indicate that ACLIP improves fine-grained defect discrimination and produces more focused anomaly evidence than representative CNN-based and CLIP-based baselines under the reported evaluation protocol.

Autoencoder-based unsupervised intrusion detection systems (IDS) offer a promising approach for identifying zero-day attacks without requiring labeled malicious data. However, residual autoencoder (RAE) frameworks remain limited by static decision thresholds, susceptibility to concept drift (CD), limited adversarial robustness, and restricted adaptability to emerging attack patterns. To address these limitations, we propose the Attention-Boosted Streaming Residual Autoencoder (ASRAE), a unified framework that integrates attention-enhanced representation learning, online CD detection with adaptive thresholding, benign-only adversarial training, post-identification few-shot meta-learning, and explainability through integrated gradients (IG). Experiments on UNSW-NB15, CIC-IDS2017, and CSE-CIC-IDS2018 show that ASRAE outperforms the evaluated unsupervised baselines, achieving ROC-AUC values of 0.952, 0.940, and 0.931, respectively. Under the controlled simulated concept-drift setting, ASRAE maintains a ROC-AUC above 0.91, while adversarial evaluation using Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) demonstrates improved robustness to feature-space perturbations. The few-shot adaptation stage further enables adaptation to held-out attack families using only a small number of labeled samples while largely preserving benign reconstruction performance. ASRAE requires an average inference time of 9.2 ms per sample in the evaluated experimental environment. Overall, the results demonstrate the effectiveness of ASRAE across the benchmark, simulated-drift, adversarial, and few-shot adaptation settings.