Weakly supervised convolutional neural network image target positioning method

The invention discloses a weakly supervised convolutional neural network image target positioning method, which comprises the following steps of: establishing a convolutional neural network classification model with a batch normalization layer, training the convolutional neural network classification model, and storing the trained convolutional neural network classification model; s2, inputting ato-be-positioned image into the convolutional neural network classification model trained in the step S1, and obtaining a feature map output by the deep convolutional layer; performing weighted fusionon the obtained feature map to obtain a saliency map; converting the obtained saliency map into a thermodynamic diagram, and superposing the thermodynamic diagram on the input image to generate a composite image; and storing or visualizing the obtained composite image to obtain a target positioning image. According to the weak supervision convolutional neural network image target positioning method using the batch normali