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Infrared small target detection (IRSTD) is a challenging task in computervision. During the last two decades, researchers' efforts are devoted toimproving detection ability of IRSTDs. Despite the huge improvement indesigning new algorithms, lack of extensive investigation of the evaluationmetrics are evident. Therefore, in this paper, a systematic approach isutilized to: First, investigate the evaluation ability of current metrics;Second, propose new evaluation metrics to address shortcoming of commonmetrics. To this end, after carefully reviewing the problem, the requiredconditions to have a successful detection are analyzed. Then, the shortcomingsof current evaluation metrics which include pre-thresholding as well aspost-thresholding metrics are determined. Based on the requirements ofreal-world systems, new metrics are proposed. Finally, the proposed metrics areused to compare and evaluate four well-known small infrared target detectionalgorithms. The results show that new metrics are consistent with qualitativeresults.

Assessing the applicability of common performance metrics for real-world infrared small-target detection