Real-time traffic cone detection for autonomous driving based on YOLOv4

A temporary road composed of traffic cones is an indispensable practical scene for the realization of automatic driving technology. However, the detection of traffic cones is a challenging issue because of their small volume and unfixed position. This work proposes a novel method that fuses colour and depth image information for traffic cone detection. Traffic cones are captured by a special machine vision system consisting of two monochrome cameras for cone distance perception and two colour cameras for cone colour acquisition. Via the YOLOv4 algorithm based on the Darknet platform and a detection result matching algorithm, the position of the traffic cone can be obtained and path planning can be performed. The results of experiments show that the proposed method can recognize red, blue, and yellow traffic cones in colour images with an average detection time of 35.46 ms and respective accuracies of 97.51%, 98.63%, and 97.29%. Compared with the previous traffic cone detection research, the proposed algorithm was found to exhibit advantages in small target sensitivity and overall detection accuracy in both static and dynamic experiments.

Automated summary

This study proposes a novel method that combines color and depth image information for traffic cone detection. The results show that this method has high accuracy and fast detection time in recognizing the color and position of traffic cones. Compared with previous research, it exhibits better performance in terms of small target sensitivity and overall detection accuracy.