Development of model-driven image de-snowing algorithm for auto-driving object detection

Abstract

In this paper, an algorithm to remove noise particles such as snow and rain that may occur in natural environments is proposed. Analyze the effects of de-noised images with the proposed algorithm can have on the object detection network for an autonomous vehicle. The research process for this will be divided into three stages. The first stage is the research preparation stage, and the hardware and data sets are built. When constructing a data set, it is linked to the algorithm evaluation method. In the second step, a development algorithm is proposed. To this end, approaches in the field of noise removal are analyzed and an approach appropriate for the research purpose is selected. And, analyze representative algorithms of the chosen approach and study image processing techniques essential for noise removal. Through this, it understands the image processing techniques essential for noise removal and identifies the constraints that occur when removing noise. And, by devising a method to overcome the constraints, a noise removal algorithm would be proposed. In the third step, the proposed algorithm is evaluated and the effect of the proposed algorithm on the autonomous driving object detection network is analyzed. To this end, an algorithm to be compared is selected by understanding the principles of several recently announced algorithms, and considering the characteristics and performance of the algorithms. To verify the effect on autonomous driving object detection, the object detection algorithm is analyzed for the effect on object detection by targeting the algorithm selected for each de-noise algorithm approach.