In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009
Figure 6. First pair of pictures: User clicks per screen required to obtain correct road mapping subject to number of image
parts already seen by our leamable detection algorithm (a) - road], road2 data (b) - road3 and road4 data. Solid squares
show necessary number of clicks if our detection algorithm is applied before user input. Empty squares show necessary
number of clicks without use of automatic detection algorithm. Y-axis shows estimated number of clicks and x-axis
represents number of processed images. Second pair of pictures: Error of automatic detection algorithm subject to number of
image parts already seen by leamable detection algorithm (c) - roadl, road data (d) - road3 and road4 data. The error is
measured as a fraction of image square misclassified by our detection algorithm. At all plots green lines correspond to roadl
and blue lines correspond to road2. Y-axis shows error rate and x-axis represents number of processed images.
are about 0.5 megapixel size and correspond to 5-10 meters
of road surface. Some of image parts with results of our
automatic detection are shown in Figure 8.
Experiments setup
We have developed a testing framework which emulates user
activity at the on-line stage. Given the classification results
and ground truth data it starts with automatic thresholds
adjusting. Gradient descent algorithm is used to determine a
set of thresholds that minimizes total area of misclassified
objects. Then user interaction is emulated as follows. At first,
our framework corrects all errors of automatic detection
which can be amended by relabeling segments of the coarsest
segmentation scale. Then testing framework emulates user-
aided error correction at subsequent segmentation scale. This
procedure is repeated up to the most detailed segmentation
scale. Total number of clicks required for errors correction is
calculated as a sum of click counts at all segmentation scales.
This statistic measures overall usability of our tool for road
mapping.
One can see total clicks count per image part measured on 4
roads from our image base in Figure 6 (a, b). Roadl and
road2 contain greater number of road defects than road3 and
road4, therefore larger number of user clicks is required for
mapping last two roads. We have compared number of clicks
required to achieve accurate mapping when user corrects
errors of our automatic detection algorithm with the number
of clicks required for mapping road surface from scratch
when no automatic detection is performed. One (?- или It can
be seen) can see that usage of automatic detection algorithm
leads to advance in usability of mapping tool.
As a matter of fact, road marking can be usually found
perfectly after processing the second or the third part image.
So, the problem of road defects detection is more
challenging. Figure 6 (c, d) demonstrates misclassified area
of road defects subject to number of image parts seen by
detection algorithm.
Figure 7 illustrates false positive and false negative error
rates of road defect by pixels on roadl data. This picture
represents usual behaviour of our system. The rate of
detected defects increases over time when more defects
examples shown to automatic detection algorithm.
In summary, overall error tends to decrease while the number
of handled images grows. The system usually starts to
distinguish road defects since two or three images have been
handled. Some road images like road3 and road4 contain a
small amount of road defects (some image parts do not
contain them at all). Although learning process is slowed
down and benefit of using interactive system is reduced on
such kind of roads however, usage of automatic detection
result still remains beneficial.
8. CONCLUSIONS AND FUTURE WORK
We have presented a tool for efficient interactive mapping of
road defects and lane marking on rectified images of road
pavement surface. Intensive use of computer vision methods
on different stages of our data processing workflow increases
usability of the tool.
The most significant drawbacks of our tool is the limitation
of using segments in user interaction stage and incapability to
correct detection results on sub-segment level. Also our
system currently is unable to accommodate to changes of the
road structure, e.g. illumination level changing. This
drawback can be eliminated if we provide on-line classifier
with concept adapting.
Figure 7. False positive and false negative rates on roadl
data subject to a number of handled road sections. Y-axis
shows error rate and x-axis represents number of processed
images.