基于边缘引导的自校正皮肤检测

Skin detection has been a widely studied computer vision topic for many years, whereas remains a challenging task. Previous methods celebrate their success in various ordinary scenarios but still suffer from fragmentary prediction and poor generalization. To address this issue, this paper proposes an edge guided network driven by a massive self-corrected skin detection dataset for robust skin detection. To be specific, a multi-task learning based network which conducts skin detection and edge detection jointly is proposed. The predicted edge map is further converged to the skin detection stream via an edge attention module. Meanwhile, to engage a large-scale of low-quality data from the human parsing task to strengthen the generalization of the network, a self-correction algorithm is adapted to prune the side effect of supervised by noisy labels with continuously polishing up those defects during the training process. Experimental results indicate that the proposed method outperforms the state-of-the-art in skin detection.