Implementation of a general real-time visual anomaly detection system via soft computing

The intelligent visual system detects anomalies or defects in real time under normal lighting operating conditions. The application is basically a learning machine that integrates fuzzy logic (FL), artificial neural network (ANN), and genetic algorithm (GA) schemes to process the image, run the learning process, and finally detect the anomalies or defects. The system acquires the image, performs segmentation to separate the object being tested from the background, preprocesses, segments, and retrieves regions. FL provides a powerful framework for knowledge representation and overcomes uncertainty and vagueness typically found in image analysis. An application prototype currently runs on a regular PC under Windows NT, and preliminary work has been performed to build an embedded version with multiple image processors. The application prototype is being tested at the Kennedy Space Center to detect anomalies along slide basket cables utilized by the astronauts to evacuate the Shuttle launch pad in an emergency