Abstract
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 the high, mid and low-level features in that specific order, which differs from many other approaches. It uses the tensor with the highest-levelled features 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.
Abstract (translated)
基于Resnet的多路径细化CNN用于物体轮廓检测。对于这项任务，我们优先考虑有效地利用Resnet的高级抽象功能，从而获得最先进的边缘检测结果。记住我们的重点，我们按照特定的顺序融合高、中、低级别的特性，这与许多其他方法不同。它以具有最高层次特征的张量为起点，将其与较低抽象层次的特征逐层结合，直至达到最低层次。我们在一个改进的Pascal VOC 2012数据集上对该网络进行训练，以进行对象轮廓检测，并在一个改进的Pascal VAL数据集上进行评估，使其达到卓越的性能和0.752的最佳数据集规模（ODS）。此外，通过对BSDS500数据集的精细培训，我们获得了最先进的边缘检测结果，ODS为0.824。
URL
https://arxiv.org/abs/1904.13353
