Object Contour and Edge Detection with RefineContourNet
A ResNet based multi path refinement CNN is used for object contour detection. For this task, we prioritise the effective utilization of the high level abstraction capability of a ResNet, which leads to state of the art results for edge detection. Keeping our focus in mind, we fuse high, mid and low level features in that specific order, which differs from many other approaches. The tensor with the highest levelled features is set as the starting point to combine it layer by layer with features of a lower abstraction level until it reaches the lowest level. We train this network on a modified PASCAL VOC 2012 dataset for object contour detection and evaluate on a refined PASCAL val dataset reaching an excellent performance and an Optimal Dataset Scale (ODS) of 0.752. Furthermore, by fine training on the BSDS500 dataset, we reach state of the art results for edge detection with an ODS of 0.824.
CAIP