# Sharpness-Based Distance Detection
###### Abstract
[18] proposed an improved method for reference-free image sharpness evaluation, which obtains the relevant edge point locations and directions by using the Canny edge detection algorithm based on the activation mechanism and the edge direction detection algorithm based on grey scale information of eight adjacent pixels, and then solves the edge width to build a histogram of the edge width. Finally, according to the performance of the three types of distance factors based on the histogram information, the type 3 distance factors are introduced into the weighted average edge-width-solving model to obtain the sharpness evaluation index. Recently, [PERSON] et al. [19] proposed to adaptively fuse high-level semantic features and low-level semantic features using attention blocks. It extracts content and distortion information from images through an image quality evaluation method based on content awareness and distortion inference. Not only does it achieve a good prediction accuracy, it also has strong generalisation performance in cross-dataset testing. This provides a scientific and effective research direction for blind image quality evaluation. With the continuous development of deep learning, many breakthroughs have been made in visual ranging. Recently, [PERSON] et al. [20] proposed a vehicle distance measurement method using YOLOv5, and the results show that the average error is 3.15% within 60 m of a two-way lane. [PERSON] et al. [21] proposed a small target pedestrian ranging method by combining YOLOv3 with a geometric relation method. [PERSON] et al. [22] achieved object distance measurement by improving YOLOv5, combining it with a binocular camera to calculate the difference between two images to achieve object distance measurement, showing an average error of 2.05% over a range of 16 m. Similarly, [PERSON] et al. [23] proposed a CNN-based method for image chunking quality evaluation. It was shown that the proposed CNN-based method for image chunking quality evaluation is a promising method for image chunking quality evaluation. improved the YOLOv5 algorithm and utilised the spatial geometry of the pixels in the image to achieve distance measurement. In this paper, distance detection of objects will be realised by combining object sharpness. By building a CNN architecture model and training it on different datasets, an evaluation score that has a higher correlation with human ratings is obtained. After the sharpness of the target object and the distance between the detector and the statistical value are obtained, draw the sharpness of the target object and the distance between the target object and the detector function curve, so that the distance information of the target object can be obtained through the sharpness measurement. This method is very different from other visual ranging methods; most of the pure visual ranging is measured by the size of the target object, but in the actual application scenario, the target object is unknown, it is difficult to know the size of the target object, and its application scope is greatly limited. The method proposed in this paper does not need to know the size of the target object, and the application range has been greatly improved. The measuring method proposed in this paper has many advantages, such as less calculation, a higher measuring accuracy and a lower cost.