The measured information is used to generate various vibration patterns based on the situation, utilizing shape memory alloy (SMA) actuators and small vibration motors. These vibration signals are provided via gloves, offering the advantage of intuitively conveying the direction and distance of obstacles. However, users must understand the different vibration patterns for each situation, and variations in tactile perception among individuals may exist. Regarding smart devices, some devices combine smart glasses with a depth camera or connect a camera with a smartwatch or smartphone to detect objects and provide auditory warnings to visually impaired individuals [12; 13]. However, these devices are limited to individuals wearing smart devices, and their implementation costs are high. A device for object detection and notification for visually impaired individuals that applies object recognition algorithms to augmented reality devices was developed by [14]. It combines a Microsoft Hololens, a wearable device, with YOLOv2, an object recognition algorithm, to convey the names of the detected objects through voice. However, this device has limitations in providing contextual information for complex situations and has high implementation costs. Representative walking aid apps for visually impaired individuals include OKO [15], Be My AI [16], and Oorion [17]. These apps utilize smartphone cameras to detect objects, obstacles, and text in real time and convey the recognized information to users via voice. Particularly, Be My AI combines augmented reality technology to offer a more intuitive user experience by guiding visually impaired users along their walking paths and providing obstacle information. While these apps are similar to this study in that they deliver the names of surrounding objects to users in real time, they have limitations, and they only convey the names of simple objects without expressing the state of complex objects in sentences. By contrast, our system integrates object recognition with natural language generation to provide voice-guided descriptive sentences composed of object information and cautionary phrases rather than just simple object names. This study proposes solving these issues by combining real-time object detection and inference using the object recognition model YOLO [18] with the large-scale natural language model KoAlpaca [19] to generate descriptive sentences that provide complex information about the recognized objects. This sentence is conveyed using text-to-speech (TTS) conversion [20], implementing a walking aid system for visually impaired individuals. The hardware setup reduces the implementation costs using a single board, webcam, and headphones without requiring expensive devices. The main contribution of this study is that the proposed method provides a function that explains the contextual situation of detected objects in natural language for visually impaired individuals. Existing assistive tools have primarily focused on replacing visual functions; however, visually impaired individuals require situational explanations of the detected obstacles. Therefore, the usefulness of assistive tools can be considerably enhanced by combining a natural language generation model to provide situational explanations. The remainder of this paper is structured as follows. Section 2 discusses related research and the characteristics of the models used in this study. Section 3 outlines multi-object recognition using YOLOv5. Section 4 presents the generation of alert sentences using KoAlpaca, detailing the training data, model training, and evaluation. Section 5 confirms the applicability of the system through the experimental results, explaining the integration and lightweight implementation of the two systems. Finally, Section 6 describes the results and contributions of our research and presents future research directions and limitations.