Camouflaged object detection (COD) is important for intelligent UAV monitoring and search-and-rescue operations. However, existing benchmarks focus primarily on natural camouflage, creating a noticeable domain shift for specific applications such as the search and rescue of individuals visually similar to their surroundings due to their clothing. To investigate this shift, we introduce CPD-UAV, a benchmark comprising 1061 high-resolution images with detailed pixel-level annotations across diverse terrains and flight altitudes. Benchmarking of seven state-of-the-art models on this dataset reveals specific challenges. Specifically, the scale variations and “vanishing boundaries” inherent in aerial perspectives can lead to boundary localization inaccuracies. Furthermore, this evaluation observes the deceptive nature of traditional metrics, such as Mean Absolute Error (MAE), when targets occupy small image proportions. To address the degradation of weak target signals during feature integration, we propose a lightweight, plug-and-play component: the Residual Gated Alignment Module (RGAM). RGAM handles scale variations by establishing semantic anchors in deep network layers, mitigating signal dilution and highlighting micro-targets against complex backgrounds. By integrating RGAM into three representative baselines, we demonstrate that the enhanced architectures achieve a competitive performance level. Quantitative results show consistent improvements in structural integrity (structure-measure, Sm) and boundary localization. Ultimately, this work provides a practical data platform and an effective algorithmic solution for advancing aerial monitoring systems.
Zhang et al. (Mon,) studied this question.