Weakly Supervised Approach for Joint Object and Lane Marking Detection

Abstract

Understanding the driving scene is critical for the safe operation of autonomous vehicles with state-of-the-art (SoTA) systems relying upon a combination of different algorithms to perform tasks for mathematically representing an environment. Amongst these tasks, lane and object detection are highly popular and have been extensively researched independently. However, their joint operation is rarely studied primarily due to the lack of a dataset that captures these attributes together, resulting in increased redundant computations that can be eliminated simply by performing these tasks together. To overcome this, we propose a weakly-supervised approach wherein, given an image from the lane detection dataset, we use a pretrained network to label different objects within a scene, generating pseudo bounding boxes used to train a network that jointly detects objects and lane lines. With an emphasis on inference speed and performance, we utilize prior works to construct two architectures based on Convolutional Neural Networks (CNNs) and 'Transformers. The CNN-based approach uses row-based pixel classification to detect and cluster lane lines alongside a single-stage anchor free object detector while sharing the same encoder backbone. Alternatively, using dual decoders, the transformer-based approach directly estimates bounding boxes and polynomial coefficients of lane lines. Through extensive qualitative and quantities experiments, we demonstrate the efficacy of the proposed architectures on leading datasets for object and lane detections and report state-of-the-art (SoTA) performance per GFLOPs.