Manipulation of Camera Sensor Data via Fault Injection for Anomaly Detection Studies in Verification and Validation Activities For AI
This provides a dataset for verification and validation activities in AI, but it is incremental as it builds on existing fault injection software.
The study tackled the problem of detecting anomalies in robotic systems by creating a database of 10,000 images (5,000 normal and 5,000 faulty) through fault injection into robot camera nodes, using seven types of image faults such as erosion and motion blur.
In this study, the creation of a database consisting of images obtained as a result of deformation in the images recorded by these cameras by injecting faults into the robot camera nodes and alternative uses of this database are explained. The study is based on an existing camera fault injection software that injects faults into the cameras of a working robot and collects the normal and faulty images recorded during this injection. The database obtained in the study is a source for the detection of anomalies that may occur in robotic systems. Within the scope of this study, a database of 10000 images consisting of 5000 normal and 5000 faulty images was created. Faulty images were obtained by injecting seven different types of image faults, namely erosion, dilation, opening, closing, gradient, motionblur and partialloss, at different times while the robot was operating.