Drawbar Eye Identification and Guiding

Abstract

The purpose of this research project is to study if a machine learning algorithm can utilize a video feed from a camera mounted on the back of a truck to detect a drawbar eye under varying environmental, instrumental and lightning conditions. In order to achieve this, lab based and real world testing data was collected from a setup which simulated connecting a truck and a trailer. With these setup cases, different video feeds were collected and later, images were extracted from this feed, which were labeled for the machine learning algorithm. In order to train the algorithm, YOLOv8n (You Only Look Once Version 8) was used, which is a real time object detection algorithm to identify objects in an image. This was used to classify the drawbar eye and its position in real time. In order to better guide the driver, distance between camera and drawbar eye was calculated using the pinhole camera principle, and here the angle of the camera was introduced. From the calculation, we had the horizontal distance (X), vertical distance (Y) and distance in depth (Z). Two additional tests were done in order to verify the accuracy of distance and height calculation, and take the distortions of a fisheye camera into account while calculating the said distances. The final detection accuracy (ability to detect position of drawbar eye with 50-95 percent accuracy of the labeled position) of the model came at around 75.4 percent.