Investigation regarding the Performance of YOLOv8 in Pedestrian Detection

Abstract: Autonomous cars have become a trending topic as cars become better and better at driving autonomously. One of the big changes that have allowed autonomous cars to progress is the improvements in machine learning. Machine learning has made autonomous cars able to detect and react to obstacles on the road in real time. Like in all machine learning, there exists no solution that works better than all others, each solution has different strengths and weaknesses. That is why this study has tried to find the strengths and weaknesses of the object detector You Only Look Once v8 (YOLOv8) in autonomous cars. YOLOv8 was tested for how fast and accurately it could detect pedestrians in traffic in normal daylight images and light-augmented images. The trained YOLOv8 model was able to learn to detect pedestrians at high accuracy on daylight images, with the model achieving a mean Average Precision 50 (mAP50) of 0.874 with a Frames per second (FPS) of 67. Finally, the model struggled especially when the images got darker which means that the YOLOv8 in the current stage might not be good as the main detector for autonomous cars, as the detector loses accuracy at night. More tests with other datasets are needed to find all strengths and weaknesses of YOLOv8.