Comparative Study on Impact of Output Modes on User Experience in Object Detection and Distance Estimation Systems

This study proposes a thorough investigation of countless object detection models, focusing uniquely on various YOLO versions. The paper begins with a thorough evaluation of object detection models including YOLO, ResNet, SSD, etc. by comparing their performance concerning their accuracy and effectiveness and their relative strengths and weaknesses in real-time object detection. Fabricating on the comparative analysis. This study proposes an innovative approach for object detection that integrates distance estimation and voice commands. The suggested research is aimed at enhancing real-time interaction with the surroundings, enabling users to receive immediate feedback about their surroundings. For identification and categorization, the model depends on live video input. Then estimates the distance of the object from the user, and finally gives an audio output. This study can be applied in various fields like robotics, autonomous vehicles, assistive technologies, and smart environments where object detection is crucial.