Browse > Article
http://dx.doi.org/10.3745/KTSDE.2022.11.1.51

Filtering-Based Method and Hardware Architecture for Drivable Area Detection in Road Environment Including Vegetation  

Kim, Younghyeon (경북대학교 전자전기공학부)
Ha, Jiseok (경북대학교 전자전기공학부)
Choi, Cheol-Ho (경북대학교 전자전기공학부)
Moon, Byungin (경북대학교 전자공학부/대학원 전자전기공학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.11, no.1, 2022 , pp. 51-58 More about this Journal
Abstract
Drivable area detection, one of the main functions of advanced driver assistance systems, means detecting an area where a vehicle can safely drive. The drivable area detection is closely related to the safety of the driver and it requires high accuracy with real-time operation. To satisfy these conditions, V-disparity-based method is widely used to detect a drivable area by calculating the road disparity value in each row of an image. However, the V-disparity-based method can falsely detect a non-road area as a road when the disparity value is not accurate or the disparity value of the object is equal to the disparity value of the road. In a road environment including vegetation, such as a highway and a country road, the vegetation area may be falsely detected as the drivable area because the disparity characteristics of the vegetation are similar to those of the road. Therefore, this paper proposes a drivable area detection method and hardware architecture with a high accuracy in road environments including vegetation areas by reducing the number of false detections caused by V-disparity characteristic. When 289 images provided by KITTI road dataset are used to evaluate the road detection performance of the proposed method, it shows an accuracy of 90.12% and a recall of 97.96%. In addition, when the proposed hardware architecture is implemented on the FPGA platform, it uses 8925 slice registers and 7066 slice LUTs.
Keywords
Road Detection; Vegetation Removal; Disparity; Stereo Vision;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 I. Benacer, A. Hamissi, and A. Khouas, "Hardware design and FPGA implementation for road plane extraction based on V-disparity approach," in Proceeding of 2015 IEEE International Symposium on Circuit and Systems (ISCAS), Lisbon, pp.2053-2056, 2015.
2 Z. Hu and K. Uchimura, "U-V-disparity: An efficient algorithm for stereovision based scene analysis," IEEE Proceedings Intelligent Vehicles Symposium, pp.48-54, 2005.
3 J. Zhang, S. Sclaroff, Z. Lin, X. Shen, B. Price, and R. Mech, "Minimum barrier salient object detection at 80 FPS," in Proceeding of 2015 IEEE International Conference on Computer Vision (ICCV), pp.1404-1412, 2015.
4 H. Alhaija, S. Mustikovela, L. Mescheder, A. Geiger, and C. Rother, "Augmented reality meets computer vision: Efficient data generation for urban driving scenes," International Journal of Computer Vision (IJCV), Vol.126, pp.961-972, 2018.   DOI
5 S. Alvares, "An exact analytical relation among recall, precision, and classification accuracy in information retrieval," Boston College, Boston Technical Report BCCS-02-01, pp. 1-22, 2002.
6 J. Fritsch, T. Kuhnl, and A. Geiger, "A new performance measure and evaluation benchmark for road detection algorithms," in Proceeding of 16th International IEEE Conference on Intelligent Transportation System (ITSC), pp.1693-1700, 2013.
7 Y. Kim, J. Ha, C.-H. Choi, and B. Moon, "A method and hardware architecture of drivable area detection based on filtering in road environment including vegetation," The KIPS Spring Conference 2021, pp.536-539, 2021.
8 M. Junker, R. Hoch, and A. Dengel, "On the evaluation of document analysis components by recall, precision, and accuracy," in Proceeding of the Fifth International Conference on Document Analysis and Recognition, ICDAR '99 (Cat. No.PR00318), pp.713-716, 1999.
9 S. Lee, J. Hyun, Y. S. Kwon, J. H. Shim, and B. Moon, "Vision-sensor-based drivable area detection technique for environments with changes in road elevation and vegetation," Journal of Sensor Science and Technology, Vol.28, No.2, pp.94-100, 2019.   DOI
10 P. Sharma, G. Singh, and A. Kaur, "Different techniques of edge detection in digital image processing," International Journal of Engineering Research and Applications, Vol.3, No.3, pp.458-461, 2013.
11 J. K. Suhr and H. G. Jung, "Enhancement of uv-disparity-based obstacle detection in urban environments," KSAE 2011 Annual Conference, pp.1293-1298, 2011.
12 C. Nocera, G. Papotto, A. Cavarra, E. Ragonese, and G. Palmisano, "A 13.5-dBm 1-V power amplifier for W-Band automotive radar application in 28-nm FD-SOI CMOS technology," IEEE Transactions on Microwave Theory and Techniques, Vol.69, No.3, pp.1654-1660, 2021.   DOI
13 E. Yurtsever, J. Lambert, A. Carballo, and K. Takeda, "A survey of autonomous driving: Common practices and emerging technology," IEEE Access, Vol.8, pp.58443-58469, 2020.   DOI
14 M. Grinberg and B. Ruf, "UAV use case: Real-time obstacle avoidance system for unmanned aerial vehicles based on stereo vision," Towards Ubiquitous Low-power Image Processing Platforms, Cham, Springer, pp.139-149, 2021.