Traffic sign detection optimization using color and shape segmentation as pre-processing system

Abstract

One of performance indicators in an autonomous vehicle (AV) is its ability to accommodate 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 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 frame per second (FPS) when You Only Look Once (YOLO) algorithm is used. This is an open access article under the CC BY-SA license.

Comparison of two deep learning methods for detecting fire hotspots

“…In the YOLO method, there are 24 convolution layers with 2 connected layers [29] and has a fast version designed to quickly find the boundary of detected objects [6]. One example of a fast version of the YOLO method is the tiny YOLO model which has 9 convolutional layers [30] is shown in Table 1.…”