EricLee0224/PAD
[NeurlPS 2023] A Dataset and Benchmark for Pose-agnostic Anomaly Detection.
This project helps quality control and inspection engineers detect defects on manufactured objects even when the objects are viewed from different angles or in varying poses. You provide images of 'normal' objects from various viewpoints, and the system learns what's typical. It then analyzes new images to identify unusual features like stains or missing parts, outputting an image with anomalies highlighted. This is ideal for manufacturing, product inspection, and robotics engineers who need robust automated visual inspection.
101 stars. No commits in the last 6 months.
Use this if your quality control process involves inspecting objects that might appear in various orientations, and you need to automatically identify subtle anomalies that current systems miss due to pose variations.
Not ideal if your inspection items are always perfectly aligned and presented in the same orientation, or if you primarily need to detect large-scale structural or logical defects across assemblies rather than fine-grained object anomalies.
Stars
101
Forks
7
Language
Python
License
MIT
Category
Last pushed
Dec 02, 2024
Commits (30d)
0
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