AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
These results show that targeted attention can improve moderate-overlap detection without the cost of an additional high-resolution head, but they also expose unresolved localization, source-domain, and false-accept limitations.
The proposed DAF-YOLO model, an enhanced YOLO11 model for detecting small defects with weak visual features on chip surfaces, incorporates a dual-pooling channel-spatial attention module (DPCSA) to strengthen salient responses in small-defect regions and suppress interference from complex background textures.
Industrial surface inspection must localize defects when annotated data are limited. We present Asymmetric CNN–ViT Residual Fusion (ACVRF), in which a convolutional neural network (CNN) generates proposals and primary region-of-interest features, while a Vision Transformer (ViT) supplies a residual with a bounded mixin...
Automatic defect detection for printed circuit boards (PCBs) is crucial for manufacturing quality and reliability, yet remains challenging due to tiny defect sizes, low visual contrast, and deployment constraints. We propose SHNet, a lightweight YOLOv8s-based detector that emphasizes shallow high-resolution representat...
The results demonstrate the effectiveness of the proposed framework for high-throughput wafer-defect inspection on desktop-class hardware, and its low parameter count makes it a promising candidate for future deployment on resource-constrained edge devices.
Steel surface defects directly affect product quality, structural reliability, and downstream manufacturing safety. Although YOLO-based detectors provide high inference speed, steel surface defect detection remains challenging because many defects in the NEU-DET dataset are small, low-contrast, irregularly shaped, and...
Printed circuit board (PCB) defect detection is critical for ensuring the performance and reliability of electronic products. However, traditional inspection methods suffer from low efficiency and poor stability. In recent years, the development of deep learning models has proceeded at a rapid pace. But existing deep l...
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