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Vision Based Dirt Detection with Deep Learning for Floor Cleaning Robots

為了解決NVIDIA Driver的問題，作者Shimaa Amin Ali Ahmed Bergies 這樣論述：

AbstractIndoor dirt area detection and localization based on modified yolov4 object detection algorithm and depth camera is the main goal of this research work. The empowerment of autonomous cleaning for the wide environment poses a challenge due to energy and time consumption. This work introduce

s a novel experimental vision strategy for cleaning robot to clean indoor dirt areas. A developed deep learning algorithm named YOLOv4-Dirt algorithm is utilized to classify if the floor is clean or not, and detects the position of the dirt areas. This system reduces the autonomous cleaning machine

energy consumption and minimize the time of the cleaning process which increases the life of the autonomous cleaning machine especially in wide buildings based on real-time object detection by deep learning YOLOv4 algorithm and RealSense depth camera. The YOLOv4 algorithm is modified by adding up sa

mpling layers to be able to detect the trash and wet areas successfully then the RealSense depth camera calculates the distance between the cleaning machine and dirt area based on the point cloud library using the robot operating system (ROS). Various classes of trash are utilized to emphasize the p

erformance of the developed cleaning system. The experiment confirms the effectiveness of the proposed autonomous cleaning system to handle the detected dirt areas with low effort and time consumption compared with other cleaning systems.