Physical characteristics and spillage detection Using multi-feature fusion

As an important part of road maintenance, the detection of road sprinkles has attracted extensive attention from scholars. However, after years of research, there are still some problems in the detection of road sprinkles. First of all, the detection accuracy of traditional detection algorithm is deficient. Second, deep learning approaches have great limitations for there are various kinds of sprinkles which makes it difficult to build a data set. In view of the above problems, this paper proposes a road sprinkling detection method based on multi-feature fusion. The characteristics of color, gradient, luminance and neighborhood information were considered in our method. Compared with other traditional methods, our method has higher detection accuracy. In addition, compared with deep learning-based methods, our approach doesn’t involve creating a complex data set and reduces costs. The main contributions of this paper are as follows: I. For the first time, the density clustering algorithm is combined with the detection of sprinkles, which provides a new idea for this field. II. The use of multi-feature fusion improves the accuracy and robustness of the traditional method which makes the algorithm usable in many real-world scenarios.