Assessing the applicability of common performance metrics for real-world infrared small-target detection
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Infrared small target detection (IRSTD) is a challenging task in computer vision. During the last two decades, researchers' efforts are devoted to improving detection ability of IRSTDs. Despite the huge improvement in designing new algorithms, lack of extensive investigation of the evaluation metrics are evident. Therefore, in this paper, a systematic approach is utilized to: First, investigate the evaluation ability of current metrics; Second, propose new evaluation metrics to address shortcoming of common metrics. To this end, after carefully reviewing the problem, the required conditions to have a successful detection are analyzed. Then, the shortcomings of current evaluation metrics which include pre-thresholding as well as post-thresholding metrics are determined. Based on the requirements of real-world systems, new metrics are proposed. Finally, the proposed metrics are used to compare and evaluate four well-known small infrared target detection algorithms. The results show that new metrics are consistent with qualitative results.
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