| abstract |
The invention provides a deep learning image target mapping and positioning method based on weak supervision information. The method includes: using image data with category labels to train two deep convolutional neural network frameworks respectively to obtain a classification model M1 and a classification model M2, and obtain global parameterized learnable pooling layer parameters; use a new classification model M2 Perform feature extraction on the test image to obtain a feature map, and obtain a preliminary positioning frame through feature category mapping and threshold method according to the feature map; use the selective search method to extract candidate regions from the test image, and use the classification model M1 to screen out the candidate frame set; Perform non-maximum value suppression processing on the preliminary positioning frame and the candidate frame to obtain the final target positioning frame of the test image. The present invention introduces a global learnable pooling layer with parameters, which can learn better feature expressions about the target category j, and effectively obtain the position information of the target object in the image by using selective feature category mapping. |