Obstacle detection based on semantic segmentation for unmanned vehicles in traffic scenes

YunXiang Liu; QianXun Guan

doi:10.1117/12.2652278

10 November 2022 Obstacle detection based on semantic segmentation for unmanned vehicles in traffic scenes

YunXiang Liu, QianXun Guan

Proceedings Volume 12331, International Conference on Mechanisms and Robotics (ICMAR 2022); 123313O (2022) https://doi.org/10.1117/12.2652278
Event: International Conference on Mechanisms and Robotics (ICMAR 2022), 2022, Zhuhai, China

Abstract

At present, the classical semantic segmentation methods can not accurately realize the obstacle instance detection of traffic scene, which makes this method can not be used in driverless system alone. In order to meet the requirements of obstacle detection accuracy in unmanned system, pixel level obstacle detection in complex driving scene is studied in this paper. A pixel level obstacle anomaly detection framework is formed by combining the maximum entropy, maximum distance and perceptual difference of uncertainty mapping with the output of dissimilar model. The framework uses uncertainty map and designs a hollow spatial pyramid pool structure of different receptive field stitching to enhance the correlation between various levels of information to improve the existing re synthesis methods to find the differences between the input image and the generated image. As a general framework, this method focuses on the trained segmentation network to ensure anomaly detection without affecting the accuracy of segmentation. The network is implemented based on pytorch framework. The experimental results on the road scene dataset cityscapes dataset show that the mlou reaches 85.7%.

Citation Download Citation

YunXiang Liu and QianXun Guan "Obstacle detection based on semantic segmentation for unmanned vehicles in traffic scenes", Proc. SPIE 12331, International Conference on Mechanisms and Robotics (ICMAR 2022), 123313O (10 November 2022); https://doi.org/10.1117/12.2652278

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KEYWORDS

Image segmentation

Data modeling

Image enhancement

Performance modeling

Image fusion

Unmanned vehicles

Convolution

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