Rethinking Evaluation of Infrared Small Target Detection

Shijian Lu (Nanyang Technological University) · Youwei Pang (Dalian University of Technology) · Xiaoqi Zhao (Yale University) · Lihe Zhang (Dalian University of Technology) · Huchuan Lu (Dalian University of Technology) · Georges Fakhri (Yale University) · Xiaofeng Liu (University of Michigan - Ann Arbor)

cross-dataset evaluationerror analysis evaluation protocols failure modes generalizationhierarchical analysis frameworkhybrid-level metricinfrared small target detectionmodel robustnessperformance scorespixel-level metricsreal-world system performancestandardized benchmarkingtarget-level metrics

As an essential vision task, infrared small target detection (IRSTD) has seen significant advancements through deep learning. However, critical limitations in current evaluation protocols impede further progress. First, existing methods rely on fragmented pixel- and target-level specific metrics, which fails to provide a comprehensive view of model capabilities. Second, an excessive emphasis on overall performance scores obscures crucial error analysis, which is vital for identifying failure modes and improving real-world system performance. Third, the field predominantly adopts dataset-specific training-testing paradigms, hindering the understanding of model robustness and generalization across diverse infrared scenarios. This paper addresses these issues by introducing a hybrid-level metric incorporating pixel- and target-level performance, proposing a systematic error analysis method, and emphasizing the importance of cross-dataset evaluation. These aim to offer a more thorough and rational hierarchical analysis framework, ultimately fostering the development of more effective and robust IRSTD models. An open-source toolkit has be released to facilitate standardized benchmarking.