SE-FPN-based target detection model training method and target detection method and device

Abstract

The invention discloses an SE-FPN-based target detection model training method and device and a target detection method and device, and the training method comprises the steps: zooming a plurality oftraining pictures according to different zooming coefficients, and splicing the training pictures into a new picture which comprises a plurality of targets of different sizes; distributing the plurality of targets with different sizes to different pyramid feature layers of an SE-FPN target detection model according to a predetermined distribution strategy; in each pyramid feature layer, finding mpositions closest to a central point according to true values of training samples of the pyramid feature layer, calculating DIoUDg of all anchors of the m positions and the true values, calculating aDg mean value mg and a standard deviation vg, obtaining a threshold tg, and selecting the central position which is larger than the tg and located in a target frame and anchor output; and calculatinga classification loss function and a position regression function, and training a model through a back propagation algorithm. The SEFPN-based target detection network model is constructed, an image preprocessing mode and a sample selection strategy are improved, the model is trained, the model is applied to target detection, and the target detection efficiency is improved.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a SE-FPN-based target detection model training method, target detection method and device. Background technique [0002] Object detection is a basic research topic in the field of computer vision, and it is widely used in areas such as unmanned driving, intelligent monitoring, and automatic object recognition. Traditional object detection methods mainly perform region selection and localization through sliding windows, and then classify them through support vector machines (SVM) and other classifiers. With the development of deep learning, convolutional neural network has achieved a series of results in target detection. Compared with traditional target detection methods, the detection method based on deep convolutional network model has the advantages of independent feature extraction and strong generalization ability. , has become one of the important research topics in the fiel...

AI Technical Summary

Problems solved by technology

However, in the data set obtained by the actual project, the scale of the target is often unevenly distributed. Most of the targets are concentrated on a certain layer of the pyramid for training, and the other two layers cannot be well trained, which not only causes waste of resources, but also affects the detection accuracy.

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