Abstract: Implementations of this specification provide prediction methods and apparatuses for adjusting computing power. One method comprises receiving a prediction request, wherein the prediction request comprises a sample to be tested, determining a computing power coefficient allocated to the prediction request, wherein the computing power coefficient indicates a proportion of hardware computing power resources allocated to the prediction request to total hardware computing power resources needed for a neural network model to run on a computing platform, determining k sub-networks inn sub-networks of the neural network model to be used for a present time based on the computing power coefficient, where n>2, and inputting the sample to be tested to the k sub-networks to obtain a prediction result.

Abstract: A method for predicting a direction of movement of a target object, a method for training a neural network, a smart vehicle control method, a device, an electronic apparatus, a computer readable storage medium, and a computer program. The method for predicting a direction of movement of a target object comprises: acquiring an apparent orientation of a target object in an image captured by a camera device, and acquiring a relative position relationship of the target object in the image and the camera device in three-dimensional space (S100); and determining, according to the apparent orientation of the target object and the relative position relationship, a direction of movement of the target object relative to a traveling direction of the camera device (S110).

Abstract: Embodiments of the present disclosure disclose forward collision control methods and apparatuses, electronic devices, programs, and media, where the forward collision control method includes: detecting a forward suspected collision object on a road where a current traveling object is located on the basis of a neural network; predicting the collision time between the current traveling object and the suspected collision object; and performing forward collision control on the current traveling object according to the collision time, where the forward collision control includes forward collision warning and/or driving control.

Abstract: Method, apparatuses, systems, electronic devices, computer readable storage media, and computer program products for controlling image acquisition are provided. In one aspect, a method includes: providing a first image sample set to a first neural network; selecting one or more first hard samples from the first image sample set according to a processing result of the first neural network for each first image sample in the first image sample set; determining acquisition environment information of the one or more first hard samples based on the one or more first hard samples; and generating, according to the acquisition environment information, image acquisition control information for instruction of an acquisition of a second image sample set comprising one or more second hard samples.

Abstract: Example detecting methods and apparatus are described.

Abstract: A 3D object detection method includes: obtaining two-dimensional (2D) coordinates of at least one predetermined key point of a target object in an image to be processed; constructing a pseudo 3D detection body of the target object in a 2D space according to the 2D coordinates of the at least one predetermined key point; obtaining depth information of a plurality of vertices of the pseudo 3D detection body; and determining a 3D detection body of the target object in a 3D space according to the depth information of the plurality of vertices of the pseudo 3D detection body.

Abstract: Disclosed in embodiments of the present disclosure are an object three-dimensional detection method and apparatus, an intelligent driving control method and apparatus, a medium, and a device. The object three-dimensional detection method comprises: obtaining two-dimensional coordinates of a key point of a target object in an image to be processed; constructing a pseudo three-dimensional detection body of the target object according to the two-dimensional coordinates of the key point; obtaining depth information of the key point; and determining a three-dimensional detection body of the target object according to the depth information of the key point and the pseudo three-dimensional detection body.

Abstract: The present disclosure relates to a method and device for recognizing indication information of indicator lights, an electronic apparatus, and a storage medium. The method comprises: acquiring an input image; determining a detection result of a target object based on the input image, the target object including at least one of an indicator light base and an indicator light in a lighted state, and the detection result including a type of the target object and a position of the target region where the target object in the input image is located; and recognizing, based on the detection result of the target object, the target region where the target object in the input image is located to obtain indication information of the target object.

Abstract: Disclosed in embodiments of the present disclosure are an object three-dimensional detection method and apparatus, an intelligent driving control method and apparatus, a medium, and a device. The object three-dimensional detection method comprises: obtaining two-dimensional coordinates of a key point of a target object in an image to be processed; constructing a pseudo three-dimensional detection body of the target object according to the two-dimensional coordinates of the key point; obtaining depth information of the key point; and determining a three-dimensional detection body of the target object according to the depth information of the key point and the pseudo three-dimensional detection body.

Abstract: A method of detecting moving object, comprises: acquiring depth information of pixels of an image to be processed; acquiring optical flow information between the image to be processed and a reference image, wherein the reference image and the image to be processed are two images that are collected by an image pickup device in a continuous photographing mode and have timing a sequence relationship; acquiring, according to both the depth information and the optical flow information, a three-dimensional motion field of the pixels of the image to be processed with respect to the reference image; and determining a moving object involved in the image to be processed according to the three-dimensional motion field. An electronic apparatus is further provided.

Abstract: A method and device for object detection, and a non-transitory computer readable storage medium are provided. The method includes the following. Object detection is performed on images of at least one second domain with a neural network to obtain detection results, where the neural network is trained with a first image sample set for a first domain. For at least one image among the images of the at least one second domain of which the detection result has a confidence level that is lower than a first threshold, the at least one image is assigned as an image sample in at least one second image sample set. At least one image sample is selected from the first image sample set and at least one image sample is selected from each of the at least one second image sample set.

Abstract: Implementation modes of the disclosure disclose an obstacle detection method, an intelligent driving control method, an electronic device, and a non-transitory computer-readable storage medium. The obstacle detection method includes that: a first disparity map of an environment image is obtained, the environment image being an image representing information of a space environment where an intelligent device is moving; multiple obstacle pixel areas are determined in the first disparity map; the multiple obstacle pixel areas are clustered to obtain at least one class cluster; and an obstacle detection result is determined according to the obstacle pixel areas belonging to the same class cluster.

Abstract: A vehicle lamp detection method includes: obtaining an image block including an image of a vehicle; and performing vehicle lamp detection on the image block by means of a deep neural network, to obtain a vehicle lamp detection result.

Abstract: A method and apparatus for multi-level target classification, a method and apparatus for traffic sign detection, an electronic device, and a computer storage medium are provided. The method for multi-level target classification includes: obtaining at least one candidate region feature corresponding to at least one target in an image, where the image includes at least one target, and each of the targets corresponds to one candidate region feature; obtaining at least one first probability vector corresponding to at least two classes based on the at least one candidate region feature, and classifying each of the at least two classes to respectively obtain at least one second probability vector corresponding to at least two sub-classes in the class; and determining a classification probability that the target belongs to the sub-class based on the first probability vector and the second probability vector.

Abstract: Provided are a method and apparatus for determining an orientation of a target object, a method and apparatus for controlling intelligent driving, an electronic device, a computer-readable storage medium and a computer program. The method for determining an orientation of a target object includes that: a visible surface of a target object in an image is acquired; position information of multiple points in the visible surface in a horizontal plane of a Three-Dimensional (3D) space is acquired; and an orientation of the target object is determined based on the position information.

Abstract: A method for predicting a direction of movement of a target object, a method for training a neural network, a smart vehicle control method, a device, an electronic apparatus, a computer readable storage medium, and a computer program. The method for predicting a direction of movement of a target object comprises: acquiring an apparent orientation of a target object in an image captured by a camera device, and acquiring a relative position relationship of the target object in the image and the camera device in three-dimensional space (S100); and determining, according to the apparent orientation of the target object and the relative position relationship, a direction of movement of the target object relative to a traveling direction of the camera device (S110).

Abstract: Embodiments of the present disclosure disclose a traffic light detection method and apparatus, an intelligent driving method and apparatus, a vehicle, and an electronic device. The traffic light detection method includes: obtaining a video stream including a traffic light; determining a candidate region of the traffic light in at least one frame of image of the video stream; and determining at least two attributes of the traffic light in the image based on the candidate region.

Abstract: A 3D object detection method includes: obtaining two-dimensional (2D) coordinates of at least one predetermined key point of a target object in an image to be processed; constructing a pseudo 3D detection body of the target object in a 2D space according to the 2D coordinates of the at least one predetermined key point; obtaining depth information of a plurality of vertices of the pseudo 3D detection body; and determining a 3D detection body of the target object in a 3D space according to the depth information of the plurality of vertices of the pseudo 3D detection body.

Abstract: Embodiments of the present disclosure disclose forward collision control methods and apparatuses, electronic devices, programs, and media, where the forward collision control method includes: detecting a forward suspected collision object on a road where a current traveling object is located on the basis of a neural network; predicting the collision time between the current traveling object and the suspected collision object; and performing forward collision control on the current traveling object according to the collision time, where the forward collision control includes forward collision warning and/or driving control.

Abstract: A vehicle lamp detection method includes: obtaining an image block including an image of a vehicle; and performing vehicle lamp detection on the image block by means of a deep neural network, to obtain a vehicle lamp detection result.