AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection

TL;DR

AffordanceNet is an end-to-end deep learning framework that detects objects and their pixel-wise affordances in RGB images, enabling real-time robotic applications with high accuracy.

Contribution

It introduces a novel multi-task deep learning architecture with specific components for joint object and affordance detection, outperforming existing methods.

Findings

Outperforms state-of-the-art methods on public datasets

Operates at 150ms per image for real-time use

Effective in diverse testing environments and robotic applications

Abstract

We propose AffordanceNet, a new deep learning approach to simultaneously detect multiple objects and their affordances from RGB images. Our AffordanceNet has two branches: an object detection branch to localize and classify the object, and an affordance detection branch to assign each pixel in the object to its most probable affordance label. The proposed framework employs three key components for effectively handling the multiclass problem in the affordance mask: a sequence of deconvolutional layers, a robust resizing strategy, and a multi-task loss function. The experimental results on the public datasets show that our AffordanceNet outperforms recent state-of-the-art methods by a fair margin, while its end-to-end architecture allows the inference at the speed of 150ms per image. This makes our AffordanceNet well suitable for real-time robotic applications. Furthermore, we demonstrate the effectiveness of AffordanceNet in different testing environments and in real robotic applications. The source code is available at https://github.com/nqanh/affordance-net