Research Article

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.
     //Calculate the derivative of each layer
(8)     ;
(9)     ;
     //Update parameters
(10)     ;
(11)     ;
(12)    end for
(13)   end for
Output: the final converged whole model.