Abstract
Vision-based unpaved road detection is a challenging task due to the complex nature scene. In this paper, a novel algorithm is proposed to improve the accuracy and robustness of unpaved road detection and boundary extraction with low computational costs. The novelties of this paper are as follows: (1) We use a normal distribution with infrared images to detect the vanishing line, and a trapezoid prediction model is proposed according to the road shape features. (2) Road recognition based on connected regions is implemented by an improved support vector machine (SVM) classifier with a normalized class feature vector. According to the recognition results, the road probability confidence map is obtained. (3) With the help of fusing continuous information with the trapezoidal forecasting model and the probability from the confidence map, we present a road probability recognition method based on the trapezoidal forecasting model and spatial fuzzy clustering. Furthermore, the histogram backprojection model is used to solve interference problems caused by shadows on the road. It takes approximately 0.012~0.014 s to process one frame of an image for the road recognition, and the accuracy rate can reach 93.2%. The experimental results show that the algorithm can achieve better performance than some state-of-the-art methods in terms of detection accuracy and speed.
Highlights
The intelligent vehicle remains a core problem in computer vision technology and has numerous potential applications, such as driver assistance, transportation system scheduling, and searching for the optimum route
(3) With the help of fusing continuous information with the trapezoidal forecasting model and the probability from the confidence map, we present a road probability recognition method based on the trapezoidal forecasting model and spatial fuzzy clustering
2 Methods we detail the proposed fuzzy Cmeans (FCM)-based unpaved road detection framework. It is designed with the following steps: (1) vanishing line detection based on the normal distribution with infrared images; (2) an image segmentation method that uses the double-Otsu algorithm and the trapezoid prediction is obtained; (3) image classification with the improved support vector machine (SVM) and construction of the probability confidence map according to the classification results; (4) spatial fuzzy clustering algorithm based on FCM combined with the trapezoidal forecasting model and the probability confidence map to complete road recognition; and (5) grayscale-based histogram backprojection to weaken the interference caused by road shadows
Summary
The intelligent vehicle remains a core problem in computer vision technology and has numerous potential applications, such as driver assistance, transportation system scheduling, and searching for the optimum route. There is no doubt that road detection has become one of the most popular topics in computer vision [1,2,3]. Computer vision technology is very suitable for road detection since it includes a large amount of detection information and accurate sensing [4, 5]. Road detection is still challenging due to different road types and various background, weather, and illumination conditions. Over the past few decades, numerous approaches have been developed for road detection. According to the structuralization degree, existing road detections can be classified into two categories: paved road detection and
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