Unmanned aerial vehicle detection method based on multi-cavity convolution and SE attention residual error

The invention provides an unmanned aerial vehicle detection method based on multi-cavity convolution and SE attention residual error, and the method comprises the steps: obtaining an unmanned aerial vehicle image sample set, dividing the sample set into a training set and a verification set, and carrying out the preprocessing of image samples in the training set; a target detection model YOLOv5 is improved to obtain an unmanned aerial vehicle target detection model, specifically, after an SPP structure of a backbone network of the target detection model YOLOv5, an SE attention residual network module is introduced; in a PANet structure of a feature fusion network of a target detection model YOLOv5, a multi-cavity convolution fusion module is introduced behind each convolution layer; constructing a GPU training environment and setting training parameters; inputting the training set and the verification set into an improved unmanned aerial vehicle target detection model for training and verification to obtain a