An HVS-derived network for assessing the quality of camouflaged targets with feature fusion

High-value assets on the battlefield typically require adequate camouflage to evade detection and annihilation by enemy scouts. Consequently, artificial camouflage technology is extensively acknowledged and utilized as a crucial defensive tactic in the military sphere. The quality of camouflage performance was assessed by military observers through the human visual system (HVS). This method involved locating the camouflaged objects and rating the camouflaged degree against the background. Current camouflage assessment methods typically involved the manual extraction and aggregation of objective features throughout an image. These approaches fall short in constructing a correlation mapping between objective features and subjective perceptions of camouflaged objects, culminating in imprecise assessments and discrepancies. To address these issues, this paper presents the first three-stage full-reference learning framework for locating camouflaged objects, extracting camouflage features, and assessing camouflage quality. Given the lack of datasets specifically designed for evaluating camouflage quality, we have contributed a datasets focused on human-camouflaged targets. The experimental results show that the three-stage framework is remarkably accurate in assessing the camouflage quality, leading to an explainable network. The camouflaged people quality assessment(CPQA) dataset is available at http://github.com/samsunq/CPQA_Datasets.git . • A three-stage learning model is proposed to measure the quality of camouflaged targets, capable of simultaneous localization, feature extraction, that is derived from the human visual system (HVS). • Collected and created the first camouflaged people evaluation assessment (CPQA) dataset. • The proposed method achieves an evaluation outcome in the CPQA dataset, which is nearly linearly correlated with the Mean Opinion Scores (MOS) and surpasses current alternative evaluation methods.