Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (3): 727-735.doi: 10.13229/j.cnki.jdxbgxb20171257

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Unstructured road detection method based on RGB entropy and improved region growing

Hua⁃yue WU(),Li⁃ren DUAN   

  1. School of Information Engineering, Chang′an University, Xi′an 710064, China
  • Received:2017-12-22 Online:2019-05-01 Published:2019-07-12

Abstract:

Unstructured roads usually do not have leading lines such as lanes, and the road boundary is vague and there exist many interference factors around the roads. Therefore it is not reliable to use the lane keeping system for autopilot and driver assistance system in this kind of roads. By generating the entropy image from original image the minimum difference of histogram of the entropy image is computed, and the difference is used as the segmental threshold to preliminarily segment the image. Then, the improved region growing method is used to extract the lane from the preliminarily segmented image. The real?time quadratic curve is used to establish the lane model, and the improved least square fitting method is applied to effectively avoid noisy points on the edges of lane region and promote the fitting speed. The experimental results show that the improved methods can be used to rapidly extract the lane from road image and fit out the lane line, therefore it helps to achieve visual based lane keeping on unstructured road for autopilot and driver assistance system.

Key words: traffic information engineering and control, unstructured road detection, RGB image entropy, region growing method, quadratic curve model

CLC Number: 

  • U495

Fig.1

Original image and its entropy image"

Fig.2

Entropy image and its segmented entropy image"

Fig.3

Process of region growing and region ID"

Fig.4

Result of improved region growing method"

Fig.5

Effect of extracting lanes using improved region growing method"

Fig.6

Process of road fitting in this paper"

Fig.7

Lane identification of different unstructured roads"

Fig.8

Lane identification of underground car parks"

Table 1

Generation time statistics of segmented"

帧率/(帧·s?1)总帧数总生成时间/ms每帧平均时间/ms
206000144 21524
12000287 54723
36000904 67125
257500157 61421
15000344 75222
450001080 41824
309000198 40622
18000414 05723
540001243 25923
3510500252 46724
21000483 19723
630001574 94525

Table 2

Time statistics of lane identification"

帧率/(帧·s?1)总帧数总识别时间/ms每帧平均时间/ms
206 000150 34725
12 000299 67925
36 000934 24925
257 500165 20722
15 000359 81424
45 0001125 57425
309 000207 58123
18 000426 90323
54 0001297 43324
3510 500253 10824
21 000504 26824
63 0001633 86925

Table 3

Lane identification result with different"

车速/(km·h?1)帧率/(帧·s?1)总帧数完成帧数

完成率

/%

识别间隔距离/(m·帧?1)
201518 00018 0001000.37
2024 00024 0001000.27
2530 00030 0001000.22
402024 00024 0001000.55
2530 00030 0001000.44
3036 00036 0001000.37
602530 00030 0001000.66
3036 00036 0001000.55
3542 00041 271980.47
803036 00036 0001000.74
3542 00040 834970.63
4048 00045 273940.55
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