Remote sensing image special vehicle target detection method based on transfer learning

Abstract

The invention provides a remote sensing image special vehicle target detection method based on transfer learning, and the method comprises the following steps: firstly obtaining vehicle image data, and obtaining training data according to the vehicle image data; inputting the training data to a ResNet network for transfer learning to obtain a target detection network; and finally, inputting to-be-detected data to the target detection network, and outputting a detection result by the target detection network. According to the invention, the convergence speed of the residual network is improved,the target detection network outputs the detection result, the detection precision is effectively improved, and the cost is reduced.

Description

technical field [0001] The invention belongs to the field of earth vision target recognition, and relates to a vehicle target detection method of a remote sensing image. Background technique [0002] Object recognition has always been a research hotspot in the field of computer vision. Currently, deep learning is widely used in the field of object recognition. The characteristic of deep learning is that the larger the sample set, the more accurate the data set can be trained. However, in the process of actual research, it is difficult to obtain a large amount of data, and the method of manual labeling of images is time-consuming and laborious. The training set and test set must meet Defects such as the same distribution will make it difficult to obtain large data sets. The expiration of training samples and the lack of training data often make it necessary to re-collect labeled data, but if the amount of data is large, it will be time-consuming, labor-intensive, and costly....

AI Technical Summary

Problems solved by technology

[0006] However, the above-mentioned patents require a large amount of training data when establishing a model. In the actual research process, defects such as the difficulty of obtaining a large amount of data, the method of manually labeling images is time-consuming and laborious, and the training set and test set must conform to the same distribution will make large The acquisition of data sets becomes difficult to achieve, and the expiration of training samples and lack of training data often make it necessary to re-collect labeled data, but if the amount of data is large, it will be time-consuming, costly, and the detection accuracy is low

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