Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Vision-sensor-based Drivable Area Detection Technique for Environments with Changes in Road Elevation and Vegetation

도로의 높낮이 변화와 초목이 존재하는 환경에서의 비전 센서 기반

  • Lee, Sangjae (School of Electronics Engineering, Kyungpook National University) ;
  • Hyun, Jongkil (School of Electronics Engineering, Kyungpook National University) ;
  • Kwon, Yeon Soo (School of Electronics Engineering, Kyungpook National University) ;
  • Shim, Jae Hoon (School of Electronics Engineering, Kyungpook National University) ;
  • Moon, Byungin (School of Electronics Engineering, Kyungpook National University)
  • Received : 2019.03.11
  • Accepted : 2019.03.17
  • Published : 2019.03.31
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Abstract

Drivable area detection is a major task in advanced driver assistance systems. For drivable area detection, several studies have proposed vision-sensor-based approaches. However, conventional drivable area detection methods that use vision sensors are not suitable for environments with changes in road elevation. In addition, if the boundary between the road and vegetation is not clear, judging a vegetation area as a drivable area becomes a problem. Therefore, this study proposes an accurate method of detecting drivable areas in environments in which road elevations change and vegetation exists. Experimental results show that when compared to the conventional method, the proposed method improves the average accuracy and recall of drivable area detection on the KITTI vision benchmark suite by 3.42%p and 8.37%p, respectively. In addition, when the proposed vegetation area removal method is applied, the average accuracy and recall are further improved by 6.43%p and 9.68%p, respectively.

Keywords

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Fig. 1. Flowchart of the proposed method.

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Fig. 2. Operation of the Sobel vertical mask in a disparity image; (a) Obstacle area, (b) Road area.

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Fig. 3. Removing obstacles in a disparity image.

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Fig. 4. Horizontal line determination process; (a) Initial v-disparity image, (b) V-disparity image after removing noises, (c) Horizontal line in the v-disparity image.

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Fig. 5. Refinement of disparities corresponding to road area.

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Fig. 6. Free space detection.

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Fig. 7. Process of vegetation detection; (a) Color image, (b) Image binarized by a channel of CIELAB color space, (c) Result of vegetation detection.

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Fig. 8. Result of the proposed drivable area detection method with vegetation area removal.

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Fig. 9. Results of drivable area detection; (a) Result of [10], (b) Result of the proposed free space detection method, (c) Vdisparity image.

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Fig. 10. Results of drivable area detection; (a) Result of [10], (b) Result of the proposed free space detection method, (c) Vdisparity image.

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Fig. 11. Results of the proposed methods; (a) Results of free space detection method, (b) Results of drivable area detection method with vegetation area removal.

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Fig. 12. Results of the proposed drivable area detection method.

Table 1. Comparison of results of drivable area detection.

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Table 2. Comparison of results of the proposed methods with and without vegetation area removal.

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