VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition (1710.06288v1)

Abstract: In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions. We tackle rainy and low illumination conditions, which have not been extensively studied until now due to clear challenges. For example, images taken under rainy days are subject to low illumination, while wet roads cause light reflection and distort the appearance of lane and road markings. At night, color distortion occurs under limited illumination. As a result, no benchmark dataset exists and only a few developed algorithms work under poor weather conditions. To address this shortcoming, we build up a lane and road marking benchmark which consists of about 20,000 images with 17 lane and road marking classes under four different scenarios: no rain, rain, heavy rain, and night. We train and evaluate several versions of the proposed multi-task network and validate the importance of each task. The resulting approach, VPGNet, can detect and classify lanes and road markings, and predict a vanishing point with a single forward pass. Experimental results show that our approach achieves high accuracy and robustness under various conditions in real-time (20 fps). The benchmark and the VPGNet model will be publicly available.

• The paper introduces VPGNet, a unified multi-task network that detects and classifies lanes and road markings while predicting vanishing points.
• It employs phase-wise training with a focus on vanishing point prediction and joint task optimization, achieving real-time 20 fps performance.
• The model shows improved precision and recall in challenging weather conditions, validated on a benchmark dataset of 20,000 images across 17 classes.

Evaluation of VPGNet for Lane and Road Marking Detection

The paper under review presents a comprehensive exploration of VPGNet, a Vanishing Point Guided Network designed to handle the intricate task of lane and road marking detection and recognition, especially in adverse conditions such as rain and low-light scenarios. This research addresses a critical gap in autonomous driving technology, which requires robust perception systems to accurately interpret driving environments under diverse weather conditions.

Core Contributions

This work introduces VPGNet, a unified end-to-end trainable multi-task network capable of performing several tasks simultaneously: detecting lanes and road markings, classifying them, and predicting the vanishing point of the scene. The paper emphasizes the robustness and real-time performance of VPGNet, established through a series of experiments under various challenging conditions.

Key contributions include:

• The creation of a comprehensive benchmark dataset encompassing around 20,000 images annotated for 17 classes of lane and road markings, specifically under four scenarios: no rain, rain, heavy rain, and night.
• The proposal of a novel network architecture that integrates a multi-task learning approach with a vanishing point prediction task to enhance the lane and road marking detection performance under challenging environments.

Methodology and Architecture

VPGNet's architecture leverages multiple tasks that operate in concert to enhance detection accuracy and efficiency. The network comprises four task-specific modules: grid regression, object detection, multi-label classification, and vanishing point prediction. The vanishing point prediction is designed to encapsulate a global geometric context akin to human-level pattern recognition, which is crucial for scenarios where lane visibility is compromised, such as during poor weather conditions.

The training of VPGNet is tackled in two phases: initially focusing on the vanishing point prediction to grasp the global scene context, followed by a joint training across all tasks to fine-tune the network and balance its performance across the integrated tasks. The network's performance is measured using precision and recall metrics, highlighting its capacity to maintain high accuracy and real-time processing speeds of 20 fps.

Results and Analysis

The experimental findings of this study reveal that VPGNet not only excels in detecting and classifying road markings across challenging environments but also significantly improves when incorporating the vanishing point prediction task. The precision-recall evaluation demonstrates superior performance compared to baseline models, with the inclusion of the quadrant-based vanishing point prediction method providing notable gains, especially under adverse conditions.

Implications and Future Work

The theoretical implications of this work suggest promising advancements in the field of autonomous vehicle perception systems. By addressing under-explored conditions such as night and heavy rain, VPGNet positions itself as a pivotal contribution to the landscape of computer vision applications in autonomous driving.

Practically, the release of the benchmark dataset alongside VPGNet fosters further research and development in the domain, encouraging the exploration of complex models that enhance the reliability and versatility of driving systems.

Future developments may explore integrating advanced contextual awareness and scene understanding mechanisms. Researchers could also leverage these findings to improve long-range perception capabilities and apply these models to other domains that require environment understanding under adverse conditions, such as robotics and surveillance systems.

In conclusion, VPGNet represents a significant step towards creating robust and adaptable perception systems for autonomous vehicles, with empirical results supporting its efficacy and practical utility in real-world applications. This research lays a strong foundation and opens avenues for further exploration in intelligent perception and automation.