Abstract
One of performance indicator of an Autonomous Vehicle (AV) is its ability to accomodate 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 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 saturation 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 FPS when YOLO algorithm is used.
Highlights
In industrial revolution 4.0 era, research in autonomous vehicle (AV) are extensively encouraged to build a modern and safer transportation
Since it is controlled by computational devices without any human as a driver, AV is claimed to lower down the accident rate by 90% [1]
The low frame rate indicates slow response of the traffic sign detection (TSD) system to respond to the external changes including the existence of traffic signs
Summary
In industrial revolution 4.0 era, research in autonomous vehicle (AV) are extensively encouraged to build a modern and safer transportation. An AV which is equipped with an enormous number of sensors (infrared, camera, proximity, etc) allows sensitive sense for the change of its surrounding to provide maximum safety for human being. Since it is controlled by computational devices without any human as a driver, AV is claimed to lower down the accident rate by 90% [1]. Since the traffic signs have a specific characteristic in colors and shapes, the segmentation of color and shape systems are good option to perform frame filtering in the pre-processing system and allows a lower workload for artificial intelligence system [3]
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