  As autonomous vehicles and autonomous racing rise in popularity, so does the
need for faster and more accurate detectors. While our naked eyes are able to
extract contextual information almost instantly, even from far away, image
resolution and computational resources limitations make detecting smaller
objects (that is, objects that occupy a small pixel area in the input image) a
genuinely challenging task for machines and a wide-open research field. This
study explores how the popular YOLOv5 object detector can be modified to
improve its performance in detecting smaller objects, with a particular
application in autonomous racing. To achieve this, we investigate how replacing
certain structural elements of the model (as well as their connections and
other parameters) can affect performance and inference time. In doing so, we
propose a series of models at different scales, which we name `YOLO-Z', and
which display an improvement of up to 6.9% in mAP when detecting smaller
objects at 50% IOU, at the cost of just a 3ms increase in inference time
compared to the original YOLOv5. Our objective is to inform future research on
the potential of adjusting a popular detector such as YOLOv5 to address
specific tasks and provide insights on how specific changes can impact small
object detection. Such findings, applied to the broader context of autonomous
vehicles, could increase the amount of contextual information available to such
systems.
