## 1 Introduction  
Visual impairment (VI) is a significant public health concern that affects people of all ages and is caused by a range of factors, including age-related eye diseases, genetic disorders, injuries, and infections [1]. The global population of individuals suffering from VI, including those who are completely blind, moderately visually impaired, and severely visually impaired, has reached more than 300 million [2]. The increasing number of visual impairment (VI) cases highlights the critical need to improve accessibility and mobility for visually impaired individuals, who face significant challenges in navigating public spaces due to the low success rate of obstacle avoidance. Therefore, governments of different countries are attempting to design various assistive living facilities for individuals with visual impairments. In the United States, guide dogs and white canes remain essential tools. In addition, the emergence of advanced technologies has also enhanced the independent mobility of individuals with visual impairments and blindness. GPS-based navigation systems, such as smartphone applications and standalone devices, provide step-by-step navigation and information about points of interest. Furthermore, obstacle detection devices and electronic travel aids, such as ultrasonic canes and wearable sensors, assist individuals in navigating their surroundings [3]. In the United Kingdom, tactile pavements and signage have been implemented in public spaces to improve accessibility and orientation [4]. The \"Haptic Radar\" system in Japan utilizes vibrations to provide real-time feedback on surrounding objects [5]. However, the accessibility of these facilities is often inadequate in older districts, leading to the use of personal navigation tools such as white canes and guide dogs [6]. While white canes are a popular option, their short range and potential interference with other pedestrians may hinder mobility in crowded spaces. Alternatively, guide dogs offer effective guidance, but their high cost and restrictions on public transportation may limit their widespread use [7]. For the existing advanced technologies, engineers and manufacturers face technical challenges in ensuring the accuracy and reliability of navigation and object detection systems [3]. In daily life, it is essential to prioritize efforts to address the challenges faced by visually impaired individuals, as the loss of eyesight can be a debilitating experience. Recently, machine learning techniques have greatly improved object recognition accuracy in computer vision [8]. This has led to the development of sophisticated models that can recognize objects in complex environments. These advancements have enabled the creation of highly accurate and reliable computer vision systems for applications such as self-driving cars, medical imaging, and surveillance. Near-field object detection can also benefit from these machine-learning techniques [9], allowing for accurate real-time detection of nearby objects. To locate the fences in the street, object detection is required, and it can be accomplished by a deep learning approach [10]. The distance information can then be utilized for various applications, including robotics, drone racing, autonomous vehicles, and navigation assistance tools [11; 12; 13; 14]. Based on the trained model on the dataset, detection of the fences from public objects by the images and videos are captured by the user camera. Detected objects and their categories are remarked on in the corresponding media content. After eliminating duplicated and low-confident detection, the result is then passed to the distance estimation process. In this article, machine learning is applied to obstacle detection of near distance in front, allowing visually impaired people to walk freely and safely. The objective of this work is to develop a new approach to navigation aid that assists visually impaired people to travel independently with confidence.