Algorithmic Study Of Garbage And Object Recognition About Sweeping Robot Based On Deep Learning

With the continuous development of the times and the continuous improvement of technology,people's lives have become better and better,and the floor sweeping robot was born as a technology to improve people's quality of life.They do not distinguish between personal belongings and garbage in the process of cleaning,and there is also the possibility of damage to personal belongings while bringing a clean environment.Nowadays,the use of image-based identification technology to allow machines to identify objects is based on image recognition,and this topic is combined with the SSD(object detection)algorithm to conduct research on the application of floor sweeping robots to distinguish between personal items and trash during the cleaning process.The research process was carried out by collecting pictures of different ground scenes in the home environment and many different kinds of pictures of private objects as the data source for the experiment,through which the SSD network was continuously trained,a network model for detecting trash and private objects was obtained,and the performance of the model was analyzed.In order to solve the problem of slow running rate of SSD algorithm on CPU and to improve the accuracy of the model,a lightweight network-based SSD algorithm is proposed,which solves the problem of CPU rate while improving the underlying network of SSD and improves the accuracy of the model in a small way.Experimental results show that the use of SSD algorithm can complete the detection of personal items and garbage.The performance of the improved SSD network compared to the traditional SSD algorithm,the average accuracy of the entire new network training model came to 92.26%,2.26%higher than the traditional SSD algorithm,CPU rate came to 5.3fps,4.63fps faster than the traditional SSD algorithm.