Abstract
Target detection plays a key role in the safe driving of autonomous vehicles. At present, most studies use single sensor to collect obstacle information, but single sensor cannot deal with the complex urban road environment, and the rate of missed detection is high. Therefore, this paper presents a detection fusion system with integrating LiDAR and color camera. Based on the original You Only Look Once (YOLO) algorithm, the second detection scheme is proposed to improve the YOLO algorithm for dim targets such as non-motorized vehicles and pedestrians. Many image samples are used to train the YOLO algorithm to obtain the relevant parameters and establish the target detection model. Then, the decision level fusion of sensors is introduced to fuse the color image and the depth image to improve the accuracy of the target detection. Finally, the test samples are used to verify the decision level fusion. The results show that the improved YOLO algorithm and decision level fusion have high accuracy of target detection, can meet the need of real-time, and can reduce the rate of missed detection of dim targets such as non-motor vehicles and pedestrians. Thus, the method in this paper, under the premise of considering accuracy and real-time, has better performance and larger application prospect.
Highlights
To improve road traffic safety, autonomous vehicles have become the mainstream of future traffic development in the world
It has good robustness to a certain degree of distortion and deformation [1] and You only look once (YOLO) is a target real-time detection model based on convolutional neural network
Based on the above deficiencies, this paper improves the original You Only Look Once (YOLO) algorithm as follows: (1) To eliminate the problem of redundant time caused by the identification of undesired targets, and according to the size and driving characteristics of common targets in traffic scenes, the total number of categories is set to six types, including {bus, car, truck, non-motor vehicle, pedestrian and others}. (2) For the issue of non-motor vehicle and pedestrian detection, this paper proposes a secondary image detection scheme
Summary
To improve road traffic safety, autonomous vehicles have become the mainstream of future traffic development in the world. Target recognition is one of the fundamental parts to ensure the safe driving of autonomous vehicles, which needs the help of various sensors. The most popular sensors include LiDAR and color camera, due to their excellent performance in the field of obstacle detection and modeling. The color cameras can capture images of real-time traffic scenes and use target detection to find where the target is located. It has good robustness to a certain degree of distortion and deformation [1] and You only look once (YOLO) is a target real-time detection model based on convolutional neural network. For the ability to learn massive data, capability to extract point-to-point feature and good real-time recognition effect [2], YOLO has become a benchmark in the field of target detection. The YOLO model was limited to static image detection, making a greater limitation in the detection of Mathematics 2018, 6, 213; doi:10.3390/math6100213 www.mdpi.com/journal/mathematics
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