Abstract: A method, a device and a terminal for generating training data is provided. The method for generating training data includes: obtaining an original image; determining a transferred image based on the image style transfer model and the original image, wherein the image style transfer model is obtained by minimizing a loss function, the loss function is determined by the original loss function the background loss function and the foreground loss function; determining the training data based on the transferred image. The difference between the generated training data and the target image is small, thereby improving the accuracy of the training model obtained by using the training data.

Abstract: An image annotating method includes: acquiring an image collected at a terminal; acquiring voice information associated with the image; annotating the image according to the voice information; and storing an annotated result of the image.

Abstract: A method for training an object identification neural network based on a distorted fisheye image includes: acquiring the distorted fisheye image, wherein the distorted fisheye image comprises at least one target object; dividing the distorted fisheye image into a plurality of zones according to a distortion degree; determining a zone to which each of the at least one target object belongs; categorizing the at least one target object according to the zone to which each of the at least one target object belongs; and inputting the distorted fisheye image and a category to which each of the at least one target object belongs into the object identification neural network to train the object identification neural network.

Abstract: An obstacle detection method and apparatus are provided. The obstacle detection method provided includes: obtaining a to-be-detected image; determining a road surface area and a non-road surface area in the to-be-detected image according to pixel information contained in the to-be-detected image; respectively determining an outermost layer contour line of the road surface area and a contour line of the non-road surface area; and when the contour line of at least one non-road surface area is located in the area contained in the outermost layer contour line of the road surface area, determining a physical object contained in the at least one non-road surface area as an obstacle. The present application is applied to a process of detecting an obstacle.

Abstract: A method for training an object identification neural network based on a distorted fisheye image includes: acquiring the distorted fisheye image, wherein the distorted fisheye image comprises at least one target object; dividing the distorted fisheye image into a plurality of zones according to a distortion degree; determining a zone to which each of the at least one target object belongs; categorizing the at least one target object according to the zone to which each of the at least one target object belongs; and inputting the distorted fisheye image and a category to which each of the at least one target object belongs into the object identification neural network to train the object identification neural network.

Abstract: A method, a device and a terminal for generating training data is provided. The method for generating training data includes: obtaining an original image; determining a transferred image based on the image style transfer model and the original image, wherein the image style transfer model is obtained by minimizing a loss function, the loss function is determined by the original loss function the background loss function and the foreground loss function; determining the training data based on the transferred image. The difference between the generated training data and the target image is small, thereby improving the accuracy of the training model obtained by using the training data.

Abstract: An image annotating method includes: acquiring an image collected at a terminal; acquiring voice information associated with the image; annotating the image according to the voice information; and storing an annotated result of the image.

Abstract: An obstacle detection method and apparatus are provided. The obstacle detection method provided includes: obtaining a to-be-detected image; determining a road surface area and a non-road surface area in the to-be-detected image according to pixel information contained in the to-be-detected image; respectively determining an outermost layer contour line of the road surface area and a contour line of the non-road surface area; and when the contour line of at least one non-road surface area is located in the area contained in the outermost layer contour line of the road surface area, determining a physical object contained in the at least one non-road surface area as an obstacle. The present application is applied to a process of detecting an obstacle.