Abstract: The present invention relates to the technical field of mine fire prevention and extinguishing, in particular to a liquid nitrogen direct injection and low-temperature foaming intelligent filling system based on mine fire area characteristics and an application method, comprising a liquid nitrogen storage tank, a liquid nitrogen direct injection system and a low-temperature foaming system, wherein the liquid nitrogen storage tank communicates with a liquid nitrogen pressurizing device through a main pipeline; a temperature control unit is arranged on the main pipeline; and the liquid nitrogen pressurizing device is connected with the liquid nitrogen direct injection system and the low-temperature foaming system, respectively, liquid nitrogen is directly injected into a foam liquid, to prepare a low-temperature foam type fire preventing and extinguishing material by means of forced convection, membrane boiling, explosion boiling and nucleate boiling between the liquid nitrogen and water.

Abstract: A system and method for binocular stereo vision is disclosed. The method includes acquiring a pair of images including a first image and a second image, the pair of images being captured by one or more cameras; obtaining a training model; determining a plurality of feature images according to the first image; determining one or more features of an object in the first image according to the training model and the plurality of feature images; determining a first area in the first image, the first area including at least one of the determined one or more features; obtaining depth information of the determined first area based on the first and second images; and determining a second area in the first image based on the determined first area and depth information, the second area including at least one of the determined first area.

Abstract: A system and method for binocular stereo vision is disclosed. The method includes acquiring a pair of images including a first image and a second image, the pair of images being captured by one or more cameras; obtaining a training model; determining a plurality of feature images according to the first image; determining one or more features of an object in the first image according to the training model and the plurality of feature images; determining a first area in the first image, the first area including at least one of the determined one or more features; obtaining depth information of the determined first area based on the first and second images; and determining a second area in the first image based on the determined first area and depth information, the second area including at least one of the determined first area.