The invention discloses a salient target detection method based on a cascade convolutional network and adversarial learning. The method comprises the following steps: 1, designing a global saliency estimator E; 2, designing a local saliency refiner R; 3, combining the global saliency estimator E and the local saliency refiner R into a generator G based on a cascade convolutional neural network forgenerating a saliency map; 4, optimizing the generator G; 5, designing an adversarial learning discriminator D to distinguish a real saliency map from a predicted saliency map generated by a generator G; and 6, the generator G and the adversarial learning discriminator D follow the CGAN strategy and are trained in a complete end-to-end manner, so that the generator G can better understand the structure information of the salient object, and a good saliency detection result is obtained. According to the method, the structural information is learned implicitly through confrontation learning, sothat significance target detection can be well carried out, and a best result is obtained on a plurality of databases.