Floor cleaning plays a major role in all places, like homes, offices, etc., in the olden days by humans. Long-term cleaning makes the person get tired, and they cannot be involved in the deep and neat cleaning process. To make cleaning work easier and tidier, an automatic floor cleaning robot has been introduced through the AI technique. The digital camera is to make the autonomous system to navigate accordingly based on the environmental analysis. The main drawback of the system is that the robot automatically turns in another direction whenever it finds obstacles like different kinds of doors, poles of furniture, cables, small garments on the floor, etc. The purpose of the system is to move the autonomous robot move freely even it find the object to make the system to involve in deep cleaning. This document mainly proposes a vision based YOLOv5 framework to detect the object during navigation. The dataset is annotated from the scratch using different labels for 300 images. A novel approach proposed in this model is multi class object detection using the YOLOv5 and 6-DOF with the help of manual dataset. This work proposes a system that aims to develop a highly accurate automated robot system using the Computer Vision Annotation Tool (CVAT) and deep learning algorithm. The high-quality camera is fixed in front of the robot. Video is captured, and it is automatically converted into an image using the python script. The image is annotated manually using CVAT Tool which is processed using the deep learning technique of the YOLOv5 algorithm. The incorporation of obstacle avoidance capabilities prevents collisions with furniture and walls, contributing to a hassle-free cleaning experience with the concept of 6-DOF.This system makes the cleaning process efficient even when it finds obstacles, best model attained a mean average precision (mAP) of 93%.