Detection of Objects and Trajectories in Real-time using Deep Learning by a Controlled Robot

Adil Sarsenov, Aigerim Yessenbayeva, Almas Shintemirov, Adnan Yazici


Nowadays, there are many different approaches to detect objects as well as to determine the trajectory of an object. Each of these approaches has its advantages and disadvantages in terms of real-time use for various applications. In this study, we propose an approach to detect objects in real-time using the YOLOv3 deep learning algorithm and plot the trajectory of an object using 2D LIDAR and depth cameras on a robot. The laser rangefinder allows us to find distances to objects from a certain angle, but does not provide accurate object detection of the object class. In order to detect the object in real-time and discover the class to which the object belongs, we formed YOLOv3 deep learning model using transfer learning on several classes from data sets of publicly accessible images. We also measured the distance to an object using a depth camera with LIDAR together to determine and estimate the trajectory of objects. In addition, these detected trajectories are smoothed by polynomial regression. Our experiments in a laboratory environment show that YOLOv3 with 2D LIDAR and depth camera on a controlled robot can be used fairly accurately and efficiently in real-time situations for the detection of objects and trajectories necessary for various applications.


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