Disclosed is a remote sensing imaging bridge detection method based on a convolutional neural network. With respect to remote sensing imaging having larger data quantities and larger image sizes, using conventional methods to detect locations of bridges therein is inefficient and time-consuming. The method first establishes a convolutional neural network model and captures from a remote sensing image a bridge image having a size w*h to serve as a training sample; each parameter of the convolutional neural network model is initialized; and the training sample is input into the model for training. During a detection process, a remote sensing image to be detected is scanned by using a window having a size w*h according to a step size of 1, so as to obtain a candidate window and to mark location information accordingly, and a bridge location in the remote sensing image to be detected is output after the candidate window is placed into the model, thus realizing detection. The method does not require performance of feature extraction on bridge images in advance, simplifying detection steps, and significantly accelerating processing of remote sensing images while also maintaining a high detection rate.
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