  Anomaly detection methods have demonstrated remarkable success across various
applications. However, assessing their performance, particularly at the
pixel-level, presents a complex challenge due to the severe imbalance that is
most commonly present between normal and abnormal samples. Commonly adopted
evaluation metrics designed for pixel-level detection may not effectively
capture the nuanced performance variations arising from this class imbalance.
In this paper, we dissect the intricacies of this challenge, underscored by
visual evidence and statistical analysis, leading to delve into the need for
evaluation metrics that account for the imbalance. We offer insights into more
accurate metrics, using eleven leading contemporary anomaly detection methods
on twenty-one anomaly detection problems. Overall, from this extensive
experimental evaluation, we can conclude that Precision-Recall-based metrics
can better capture relative method performance, making them more suitable for
the task.
