The dataset is already organized in YOLO format in the steel_dataset/ directory. If you need to reorganize from original format, see utility/reorganize_dataset.py. steel-defect-detection/ ├── ...
Photo-induced force microscopy (PiFM) is a sophisticated nanoscale characterization approach that combines the elevated spatial resolution of atomic force microscopy (AFM) with infrared (IR) ...
Ford has launched two AI systems to help factory workers find minor parts problems. The system checks if the correct trim parts are added to each vehicle and if each electrical connection is fully ...
TDK SensEI’s edgeRX Vision system, powered by advanced AI, accurately detects defects in components as small as 1.0×0.5 mm in real time. Operating at speeds up to 2000 parts per minute, it reduces ...
Defect detection requirements on the order of 10 defective parts per million (DPPM) are driving improvements in inspection tools’ resolution and throughput at foundries and OSATs. However, defects ...
Software defect prediction and cost estimation are critical challenges in software engineering, directly influencing software quality and project management efficiency. This study presents a ...
A MATLAB project for shape and traffic sign recognition using classical image processing techniques. It includes color segmentation, feature extraction (Hu moments, HOG, LSS), and classification with ...
Abstract: In the area of manufacturing, ensuring the integrity of structural elements of steel parts is important for safety and quality control The traditional systems of defect detection in steel ...
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