<![CDATA[Accessibility challenges for the visually impaired are getting more serious yearly. To address this issue, this study presents an advanced object detection system that utilizes YOLOv8, enhanced with OpenCV for distance estimation. The methodology involves data preparation with diverse scenarios to test system accuracy, including environments like busy streets and indoor settings. Precision, recall, and F1-score metrics evaluate performance under varying lighting conditions. Results show a decrease in performance during low-light conditions, emphasizing the need for adequate lighting for effective detection. The system also includes a real-time implementation with a panic button feature, allowing immediate activation of object detection and distance estimation processes. The results are translated into Indonesian using a translation service and converted to speech, making the information accessible to users. By integrating YOLOv8 and OpenCV, the research achieves an average object detection accuracy of 91% with a low error rate of about 3.6%. Rigorous testing and evaluation under various conditions ensure reliability and effectiveness. The implications of this research extend to real-time applications like navigation assistance for the visually impaired, highlighting the potential for improved quality of life and independence. Future work will focus on optimizing detection in low-light conditions, incorporating additional sensors like infrared cameras, and enhancing real-time text translation services for accurate information delivery to visually impaired users. Additionally, continuous training with diverse datasets will be conducted further to improve the robustness and accuracy of the detection system.]]>
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