Pavement Disease Detection through Improved YOLOv5s Neural Network
Table 1
Training process.
Algorithm 1. training of Ghost-YOLOv5s
Input: Training set , validation set , learning rate , momentum , regularization factor , number of network layers , number of neurons ,, and model training parameters (e.g., ).
(1) Initialize the network parameters using Gaussian.
(2) Randomly sort the samples of the training set;
//Train the Ghost-YOLOv5s Network
(3) fordo
(4) fordo
(5) Pick samples from the training set D;
(6) Feedforward computes the layer input and activation value for each layer until the next layer.
(7) Backpropagation calculates the error for each layer.