Pushing the Boundaries of Boundary Detection using Deep Learning

TL;DR

This paper demonstrates that adapting deep convolutional neural networks with a specialized loss, multi-resolution architecture, and external data significantly advances boundary detection, achieving near-human performance and benefiting semantic segmentation.

Contribution

It introduces a novel deep learning boundary detection method combining a tailored loss, multi-resolution design, and external data, surpassing previous state-of-the-art results.

Findings

Achieved F-measure of 0.808 on BSD dataset, close to human performance.

Improved boundary detection accuracy by integrating deep learning with grouping techniques.

Enhanced semantic segmentation performance using the proposed boundary detector.

Abstract

In this work we show that adapting Deep Convolutional Neural Network training to the task of boundary detection can result in substantial improvements over the current state-of-the-art in boundary detection. Our contributions consist firstly in combining a careful design of the loss for boundary detection training, a multi-resolution architecture and training with external data to improve the detection accuracy of the current state of the art. When measured on the standard Berkeley Segmentation Dataset, we improve theoptimal dataset scale F-measure from 0.780 to 0.808 - while human performance is at 0.803. We further improve performance to 0.813 by combining deep learning with grouping, integrating the Normalized Cuts technique within a deep network. We also examine the potential of our boundary detector in conjunction with the task of semantic segmentation and demonstrate clear improvements over state-of-the-art systems. Our detector is fully integrated in the popular Caffe framework and processes a 320x420 image in less than a second.