Disclosed in the present invention are a deep learning-based weakly supervised salient object detection method and system, the method comprising: generating salient images of all training images by using an unsupervised salient detection method; training a multi-task full convolutional neural network by using the salient images and corresponding image-level type labels as noisy supervision information for initial iteration, and generating a new type activation image and a salient object prediction image after the training process is converged; adjusting the type activation image and the salient object prediction image by using a conditional random field model; updating saliency labeling information for the next iteration using a label updating policy; performing a training process by multiple iterations until a stop condition is met; and performing general training on a data set comprising unknown types of images so as to obtain a final model. According to the present invention, noise information is automatically eliminated in an optimization process, and a good prediction effect may be achieved by only using image-level labeling information, thereby avoiding a complex and time-consuming pixel-level manual labeling process.
DEEP LEARNING-BASED WEAKLY SUPERVISED SALIENT OBJECT DETECTION METHOD AND SYSTEM