# Sharpness-Based Distance Detection
###### Abstract  
With the advancement of artificial intelligence, visual ranging has become a widely researched field. This paper introduces a novel method for distance measurement by evaluating the sharpness of objects in the current frame. It is well known that the image is sharpest at the camera's focal point and becomes blurry when moving away from it. Leveraging this characteristic, this study utilizes sharpness to achieve distance measurement. Initially, the specific orientation of the target object is identified and located. The image area of the target object is cropped in a certain direction, and its image quality is assessed through sharpness calculation. Subsequently, the relationship between sharpness and distance values is analysed statistically, and a function curve depicting their correlation is plotted. Consequently, the distance between the target object and the detector can be determined based on sharpness with an extremely small margin of error using this method. sharpness assessment; distance measurement; image quality; visual ranging +
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object, usually appearing in the position. When migrating to other areas of detection, it can maintain model AP as well as DPM, which is a strong modeling method for image space. After YOLO target detection, the target object area is extracted, and then the image quality of the area where the target object is located is evaluated. In general, image quality assessment can be divided into full-reference, partial-reference and no-reference methods. The commonly used objective evaluation of full-reference image quality is mainly based on three aspects of pixel statistics, information theory, and structural information, such as the peak signal-to-noise ratio (PSNR), the information fidelity criterion (IFC), the structural similarity (Structural Similarity Index, SSIM), and so on. Partial reference also becomes semi-reference, which takes part of the feature information of the ideal image as a reference and compares and analyses the image to be evaluated, so as to obtain the result of image quality evaluation. While reference-free image quality assessment is a challenging computer vision problem, the current reference-free image quality assessment is mainly performed using end-to-end deep learning models for pixel-level image quality assessment. With the rapid development of deep learning technology, significant progress has been made in the field of image quality evaluation [14]. The main advantages of deep learning are the ability to build end-to-end feature learning systems instead of manually produced features, and the ability of deep learning to automatically extract and generalise features from training samples to form effective quality prediction models. Researchers have attempted to further improve the quality assessment of images by utilising the powerful extraction of features as a function of convolutional neural networks (CNNs). Through continuous research on convolutional neural networks, [PERSON] et al. [15] evaluated image quality through an approach based on an optimised convolutional neural network structure, aiming at automatically extracting distinctive image quality features, improving the network learning capability and predicting the evaluation scores through normalisation and packet loss. [PERSON] et al. [16] proposed a CNN-based method for image chunking quality evaluation. It was shown that the method has strong advantages in terms of image compression time and compression efficiency. [PERSON] et al. [17] proposed a new reference-free image quality evaluation method using a migration learning technique. The feature-sharing process is optimised by introducing a network called TTL which focuses on the transfer of semantic features. After several experiments, it is shown that the network has good generalisation ability and can effectively handle different types of images. [PERSON] et al.