Forward vehicle detection method based on improved YOLOv4
Aiming at the problems that detection precision is not high,real-time performance is insufficient and detection ability of small targets is weak,that the existing deep learning algorithms has for vehicle detection,a forward vehicle detection method based on improved YOLOv4 is proposed.Aiming at the problem of high false negative rate of small targets,dense connection block(Dense Block)are used to replace the residual blocks in CSPResNet to reuse feature information and improve the detection ability of small targets.Aiming for the requirements of detection precision and real-time,the dense connection block structure is modified to convolution,batch normalization and activation operations,and the convolutional layer and batch normalization layer in the structure are fused and the Mish activation function is used to improve the detection precision and speed of the model.Experimental results show that the improved YOLOv4 algorithm reduces the number of parameters by 23%compared with the original algorithm,and the detection speed and precision are increased by3.5fps and2.73%,respectively,and the detection ability of small targets is improved.
万方数据
维普
