We propose a real-time YOLOv5 based deep convolutional neural network for detecting ships in the video. We begin with two famous publicly available SeaShip datasets each having around 9,000 images. We then supplement that with our self-collected dataset containing another thirteen thousand images. These images were labeled in six different classes, including passenger ships, military ships, cargo ships, container ships, fishing boats, and crane ships. The results confirm that YOLOv5s can classify the ship's position in real-time from 135 frames per second videos with 99 % precision.