A remote-sensing image target detection method based on a smooth bounding box regression function, comprising: performing necessary preprocessing on a training image, and setting a hyperparameter of network training; inputting a picture into a target detection convolutional neural network to obtain a feature map; then inputting the feature map into a region suggestion network to obtain a candidate box; and then sending the candidate box and the feature map into a region-of-interest pooling layer to obtain features of a region of interest, and classifying, in a classifier, the features of the region of interest; sending the obtained features of the region of interest into a full connection layer to obtain a predicted offset, then sending the predicted offset into the smooth bounding box regression function to obtain an actual offset, and correcting the candidate box to a new position; repeating the steps until a training process is finished; and preprocessing an image to be detected and then inputting same into a trained network to obtain a target detection result. High-precision bounding box regression can be effectively realized, and higher-precision target detection can be realized under the condition of a high IoU threshold.
REMOTE-SENSING IMAGE TARGET DETECTION METHOD BASED ON SMOOTH BOUNDING BOX REGRESSION FUNCTION