TELKOMNIKA Telecommunication, Computing, Electronics and Control
Traffic sign detection optimization using color and shape segmentation as pre-processing system

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Traffic sign detection optimization using color and shape segmentation as pre-processing system

Color segmentation
Optimization
Shape segmentation
Traffic sign detection

One of performance indicators in an autonomous vehicle (AV) is its ability to accom-
modate rapid environment changing; and performance of traffic sign detection (TSD)

system is one of them. A low frame rate of TSD impacts to late decision making and
may cause to a fatal accident. Meanwhile, adding any GPU to TSD will significantly
increases its cost and make it unaffordable. This paper proposed a pre-processing
system for TSD which implement a color and a shape segmentation to increase the
system speed. These segmentation systems filter input frames such that the number of
frames sent to artificial intelligence (AI) system is reduced. As a result, workload of
AI system is decreased and its frame rate increases. HSV threshold is used in color

segmentation to filter frames with no desired color. This algorithm ignores the satura-
tion when performing color detection. Further, an edge detection feature is employed

in shape segmentation to count the total contours of an object. Using German traffic
sign recognition benchmark dataset as model, the pre-processing system filters 97% of
frames with no traffic sign objects and has an accuracy of 88%. TSD system proposed
allows a frame rate improvement up to 32 frame per second (FPS) when You Only
Look Once (YOLO) algorithm is used.

Handoko, Jehoshua Hanky Pratama, Banu Wirawan Yohanes

http://journal.uad.ac.id/index.php/TELKOMNIKA

Jul 6, 2020

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