Adaptive Feature Pyramid Network to Predict Crisp Boundaries via NMS Layer and ODS F-Measure Loss Function

Adaptive Feature Pyramid Network to Predict Crisp Boundaries via NMS Layer and ODS F-Measure Loss Function

Blog Article

Edge detection is one of the fundamental computer vision tasks.Recent methods for edge detection based on a convolutional neural network (CNN) typically employ the weighted cross-entropy loss.Their predicted results being thick and needing post-processing before calculating the optimal dataset scale (ODS) F-measure for evaluation.To achieve end-to-end training, we propose a non-maximum suppression layer (NMS) to obtain sharp boundaries without the need for post-processing.

The ODS F-measure can be equi-jec 7 calculated based on these sharp boundaries.So, the ODS F-measure loss function is proposed to train the network.Besides, we propose an read more adaptive multi-level feature pyramid network (AFPN) to better fuse different levels of features.Furthermore, to enrich multi-scale features learned by AFPN, we introduce a pyramid context module (PCM) that includes dilated convolution to extract multi-scale features.

Experimental results indicate that the proposed AFPN achieves state-of-the-art performance on the BSDS500 dataset (ODS F-score of 0.837) and the NYUDv2 dataset (ODS F-score of 0.780).