High-definition image small target detection method based on auto-encoder and YOLO algorithm

Abstract

The invention discloses a high-definition image small target detection method based on an auto-encoder and a YOLO algorithm. The method mainly solves the problem that the accuracy and the speed of high-definition image small target detection cannot be considered at the same time in the prior art. The method comprises the following steps: 1) collecting and marking a high-definition image to obtaina training set and a test set; 2) performing data expansion on the marked training set; 3) generating corresponding Mask data according to marking information; 4) building an auto-encoder model; 5) training by using the training set; 6) splicing the trained coding network of the auto-encoder with a YOLO-V3 detection network to obtain a hybrid network, and training the hybrid network by using the training set; and 7) carrying out target detection on the test set by using the trained hybrid network. The calculation amount of target detection is reduced, the detection speed is improved, the detection precision of small targets in high-definition images is improved under the condition that the detection speed is guaranteed, and the method can be used for target recognition of aerial images ofunmanned aerial vehicles.

Description

technical field [0001] The invention belongs to the technical field of target detection, and in particular relates to a detection method for small targets in high-definition images, which can be used for target recognition in aerial images of unmanned aerial vehicles. [0002] technical background [0003] At present, with the development of target detection technology, especially in recent years, target detection algorithms based on deep learning have been proposed, such as Faster-RCNN, SSD series, and YOLO series. Compared with traditional target detection algorithms, these algorithms are based on deep learning. The target detection algorithm greatly surpasses the traditional detection algorithm in terms of accuracy and efficiency. However, the current algorithms are optimized based on existing data sets, such as ImageNet, COCO, etc. In practical applications, such as UAV aerial photography target detection, due to the high flying height of the UAV, the collected image size...

AI Technical Summary

Problems solved by technology

The advantage of this method is that by cropping, the spatial information of the image is guaranteed not to be lost, and it will have a good effect on the accuracy of target detection. However, since one image is cropped into multiple images, the speed of target detection will multiply

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