  Object detection for street-level objects can be applied to various use
cases, from car and traffic detection to the self-driving car system.
Therefore, finding the best object detection algorithm is essential to apply it
effectively. Many object detection algorithms have been released, and many have
compared object detection algorithms, but few have compared the latest
algorithms, such as YOLOv5, primarily which focus on street-level objects. This
paper compares various one-stage detector algorithms; SSD MobileNetv2 FPN-lite
320x320, YOLOv3, YOLOv4, YOLOv5l, and YOLOv5s for street-level object detection
within real-time images. The experiment utilizes a modified Udacity Self
Driving Car Dataset with 3,169 images. Dataset is split into train, validation,
and test; Then, it is preprocessed and augmented using rescaling, hue shifting,
and noise. Each algorithm is then trained and evaluated. Based on the
experiments, the algorithms have produced decent results according to the
inference time and the values of their precision, recall, F1-Score, and Mean
Average Precision (mAP). The results also shows that YOLOv5l outperforms the
other algorithms in terms of accuracy with a<EMAIL_ADDRESS>of 0.593, MobileNetv2
FPN-lite has the fastest inference time among the others with only 3.20ms
inference time. It is also found that YOLOv5s is the most efficient, with it
having a YOLOv5l accuracy and a speed almost as quick as the MobileNetv2
FPN-lite. This shows that various algorithm are suitable for street-level
object detection and viable enough to be used in self-driving car.
