Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network

•We propose a novel architecture called Edge-based Reversible Re-calibration Network (ERRNet) for camouflaged object detection. It advances the state-of-the-art performance with real-time inference speed on eight challenging datasets.•We design an aggregation strategy, Selective Edge Aggregation (SEA), to obtain initial edge prior, which can well alleviate the “ambiguous” problem of weak boundaries.•To model the cross-comparison stage of visual perception, we further design a multivariate calibration strategy, termed Reversible Re-calibration Unit (RRU), which re-calibrates the coarse inference map by considering diverse priors.