Use of Anomaly Detection and Object Detection as Basic Support in the Recognition of Outlier Data in Images

Despite significant advances in object detection in images, the ability to identify outlier objects in an image remains an unsolved problem. Its challenges arise due to the inherent variability of an outlier object, the limitations of the object detection approach, and the subjective nature of anomalies in different contexts. In addition, traditional object detection approaches focus on identifying previously known objects, which limits their effectiveness in detecting outlier objects. An comparative analysis based on anomaly detection and object detection is then proposed in this preliminary study, techniques that under training would allow learning a “normal” representation of the data, so that they can detect anomalies when reconstructing new data instances. If the reconstruction of a new data differs significantly from the “normal” data, it could indicate the presence of an outlier object.