Computer vision based real-time pixel-level railroad track components detection system
Systems, methods and devices for a computer vision-based pixel-level rail components detection system using an improved one-stage instance segmentation model and prior knowledge, aiming to inspect railway components in a rapid, accurate, and convenient fashion.
1. An improved one-stage object detection model comprising:
at least one camera;
at least one graphics processing unit;
at least one one-stage object detection model YOLOv4-hybrid, comprising
Swish;
Leaky-ReLU; and
a combination of Mish and Swish; and
the one-stage object detection model YOLOv4-hybrid employs a hybrid activation function, which includes parameters:
precision;
recall;
mAP; and
F1 score functionality.
2. The improved one-stage object detection model of claim 1 , wherein the model functions in diverse light conditions.
3. The improved one-state object detection model of claim 1 , wherein the model can detect image modification.
4. The improved one-state object detection model of claim 3 , wherein detection of image modification includes analysis of a structural integrity of a component analyzed by the model.
5. The improved one-state object detection model of claim 1 , further comprising a deep learning algorithm.
6. The improved one-state object detection model of claim 1 , wherein the model performs real time component detection.
7. The improved one-state object detection model of claim 1 , wherein the model performs real time component detection on a railway.