Real-time road surface marking detection from a bird’s-eye view image using convolutional neural networks

Jungyu Kang; Yongwoo Jo; Dongjin Lee; Seung-Jun Han; Kyoungwook Min; Jeongdan Choi

doi:10.1117/12.2556355

31 January 2020 Real-time road surface marking detection from a bird’s-eye view image using convolutional neural networks

Jungyu Kang, Yongwoo Jo, Dongjin Lee, Seung-Jun Han, Kyoungwook Min, Jeongdan Choi

Author Affiliations +

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 1143327 (2020) https://doi.org/10.1117/12.2556355
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

This paper considers a method for detection of road surface markings using a camera mounted on top of a vehicle. The detection is done with an orientation-aware detector based on a convolutional neural network. To successfully detect the orientation and position of road surface markings, the input frontal image is converted to a bird’s-eye view image using inverse perspective matching. Synthetic image dataset is constructed with aid of MSER (maximally stable extremal regions) algorithm to solve data imbalance problem. The detector is trained to estimate orientations of the detected objects in addition to the class labels and positions. Pretrained DenseNet based YOLOv2 model is modified to detect rotated rectangles with an additional cost function and new efficient IOU (intersection of union) measure. Instead of directly estimating the orientation angle of the road surface markings, probabilistic estimation is done with quantized angular bins. Benchmark dataset is formulated for evaluation and the experimental results showed that the considered algorithm provides promising result while running in a real-time.

Citation Download Citation

Jungyu Kang, Yongwoo Jo, Dongjin Lee, Seung-Jun Han, Kyoungwook Min, and Jeongdan Choi "Real-time road surface marking detection from a bird’s-eye view image using convolutional neural networks", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 1143327 (31 January 2020); https://doi.org/10.1117/12.2556355

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KEYWORDS

Roads

Detection and tracking algorithms

Sensors

Convolutional neural networks

Cameras

Target detection

Unmanned vehicles

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