Weakly-Supervised Free Space Estimation through Stochastic Co-Teaching

Abstract

Free space estimation is an important problem for autonomous robot navigation. Traditional camera-based approaches train a segmentation model using an annotated dataset. The training data needs to capture the wide variety of environments and weather conditions encountered at runtime, making the annotation cost prohibitively high. In this work, we propose a novel approach for obtaining free space estimates from images taken with a single roadfacing camera. We rely on a technique that generates weak free space labels without any supervision, which are then used as ground truth to train a segmentation model for free space estimation. Our work differs from prior attempts by explicitly taking label noise into account through the use of Co-Teaching. Since Co-Teaching has traditionally been investigated in classification tasks, we adapt it for segmentation and examine how its parameters affect performances in our experiments. In addition, we propose Stochastic Co-Teaching, which is a novel method to select clean samples that leads to enhanced results. We achieve an IoU of 82.6%, a Precision of 90.9%, and a Recall of 90.3%. Our best model reaches 87% of the IoU, 93% of the Precision, and 93% of the Recall of the equivalent fully-supervised baseline while using no human annotations. To the best of our knowledge, this work is the first to use Co-Teaching to train a free space segmentation model under explicit label noise. Our implementation and models are freely available online.

Striving for Less: Minimally-Supervised Pseudo-Label Generation for Monocular Road Segmentation

“…Rather than explicitly relying on scene geometry, another successful method is to identify the road by over-segmenting frames into superpixels and extracting feature vectors for each superpixel using a network trained for generic image classification. Superpixels can then be clustered in feature-space before using a spatial prior to identify the cluster corresponding to the road [4,6,7]. Our unsupervised approach unifies the geometrical and semantic approaches by combining features extracted from a v-disparity representation with cues obtained from over-segmenting the RGB space into superpixels.…”

“…The notable differences are that they obtain disparity maps through stereo depth reconstruction rather than from a monocular depth estimation network, and they simply threshold in v-disparity space to obtain a road mask, while we use cues from the RGB image in the form of superpixel segments. The other three methods [4,6,7], rely on the same pseudo-labeling strategy based on computing superpixel features from an ImageNet pretrained-network. Our method does not need stereo pairs, but still achieves the highest IoU (0.8529) and Recall (0.9623).…”