Semi-obstructed Object Detection in Water Cannon Shooting Based on YOLOv5-SSA
When the ship's water cannon is shooting target,it is easy to produce splashes,which can block the shooting tar-get.The existing water cannon control and tracking system is easy to lose the target in the water cannon shooting scene,and it is dif-ficult to obtain an effective aiming effect.This paper proposes an object detection method based on deep learning,which takes the image taken during the shooting of the water cannon as input,and detects the position of the target to be shot in real time to correct the effect of object tracking and achieve accurate and effective shooting of the target.In this paper,a lightweight deep learning ob-ject detection network YOLOv5-SSA is designed to detect targets fired by water cannons in real-time.When tested in actual scenes,the processing speed reaches 21.18ms under the premise that the object detection precision reaches 93.2%,and it achieves real-time detection of the target.
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