Machine learning-driven carrier risk modeling enables supply chains to predict and prevent pickup defects, reducing costs and improving on-time performance.

Abstract: Fabric defect detection is indispensable but challenging due to the diversity of fabric texture and defect types in textile mills, and a variety of deep learning-based supervised methods ...

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A production-ready AI vision system for real-time defect detection and classification in manufacturing environments. Achieves >95% accuracy with <100ms latency on edge hardware (NVIDIA Jetson, RTX).

Abstract: Efficient and accurate detection of surface defects on trains is crucial for ensuring train safety. However, the insufficient defect samples and their diverse patterns make defect detection ...

Two years after a panel flew off a 737 Max, Boeing is doing more inspections, completing work in its intended order and making other changes. Can the company keep it up? The number of 737 Max planes ...

Wood, a widely distributed renewable resource, plays a vital role in accelerating urbanisation. However, wood grain defects pose significant safety hazards. Detecting these defects is challenging due ...

Scientists from the federally funded Argonne National Laboratory in Illinois and the University of Virginia have developed a new approach for detecting defects in metal parts produced by 3D printing.

This repository contains the code and resources for a PCB defect detection project. The project uses YOLO and other comparative models to detect and classify PCB defects, along with improvements to ...