Abstract: Provided are a model generation method and apparatus, an object detection method and apparatus, a device and a storage medium. The method includes acquiring multiple scaling coefficients of a batch normalization layer in an initially-trained intermediate detection model, where the intermediate detection model is obtained by training an original detection model based on multiple training samples, and each training sample includes a sample image and a sample annotation result of a known object in the sample image; screening a to-be-pruned coefficient from the multiple scaling coefficients according to values of the multiple scaling coefficients; and screening a to-be-pruned channel corresponding to the to-be-pruned coefficient from multiple channels of the intermediate detection model and performing channel pruning on the to-be-pruned channel to generate an object detection model.

Abstract: An image processing method implemented by a processor includes receiving an image, acquiring a target image that includes an object from the image, calculating an evaluation score by evaluating a quality of the target image, and detecting the object from the target image based on the evaluation score.

Abstract: Provided are a model generation method and apparatus, an object detection method and apparatus, a device and a storage medium. The method includes acquiring multiple scaling coefficients of a batch normalization layer in an initially-trained intermediate detection model, where the intermediate detection model is obtained by training an original detection model based on multiple training samples, and each training sample includes a sample image and a sample annotation result of a known object in the sample image; screening a to-be-pruned coefficient from the multiple scaling coefficients according to values of the multiple scaling coefficients; and screening a to-be-pruned channel corresponding to the to-be-pruned coefficient from multiple channels of the intermediate detection model and performing channel pruning on the to-be-pruned channel to generate an object detection model.

Abstract: Disclosed is a liveness test method and apparatus. A liveness test apparatus determines a pre-liveness score based on a plurality of sub-images acquired from an input image, determines a post-liveness score based on a recognition model for recognizing an object included in the input image, and determines a liveness of the object based on any one or any combination of the pre-liveness score and the post-liveness score.

Abstract: An image processing method implemented by a processor includes receiving an image, acquiring a target image that includes an object from the image, calculating an evaluation score by evaluating a quality of the target image, and detecting the object from the target image based on the evaluation score.

Abstract: An image processing method implemented by a processor includes receiving an image, acquiring a target image that includes an object from the image, calculating an evaluation score by evaluating a quality of the target image, and detecting the object from the target image based on the evaluation score.

Abstract: An image processing apparatus, includes an image classifier configured to determine whether an input image is a low-quality image or a high-quality image; and an image evaluator configured to determine a first predetermined number of clearest images from a plurality of low-quality images determined by the image classifier.

Abstract: A processor implemented image processing method includes acquiring a facial image by performing a face detection on an image, performing a quality assessment on the facial image using a preset facial image regression model, and determining a quality level of the facial image based on the quality assessment.

Abstract: Disclosed is a liveness test method and apparatus. A liveness test apparatus determines a pre-liveness score based on a plurality of sub-images acquired from an input image, determines a post-liveness score based on a recognition model for recognizing an object included in the input image, and determines a liveness of the object based on any one or any combination of the pre-liveness score and the post-liveness score.

Abstract: A method of detecting a target includes determining a quality type of a target image captured using a camera, determining a convolutional neural network of a quality type corresponding to the quality type of the target image in a database comprising convolutional neural networks, determining a detection value of the target image based on the convolutional neural network of the corresponding quality type, and determining whether a target in the target image is a true target based on the detection value of the target image.

Abstract: Disclosed is a liveness test method and apparatus. A liveness test apparatus determines a pre-liveness score based on a plurality of sub-images acquired from an input image, determines a post-liveness score based on a recognition model for recognizing an object included in the input image, and determines a liveness of the object based on any one or any combination of the pre-liveness score and the post-liveness score.

Abstract: Disclosed is a liveness test method and apparatus. A liveness test apparatus determines a pre-liveness score based on a plurality of sub-images acquired from an input image, determines a post-liveness score based on a recognition model for recognizing an object included in the input image, and determines a liveness of the object based on any one or any combination of the pre-liveness score and the post-liveness score.

Abstract: A processor implemented image processing method includes acquiring a facial image by performing a face detection on an image, performing a quality assessment on the facial image using a preset facial image regression model, and determining a quality level of the facial image based on the quality assessment.

Abstract: An image processing method implemented by a processor includes receiving an image, acquiring a target image that includes an object from the image, calculating an evaluation score by evaluating a quality of the target image, and detecting the object from the target image based on the evaluation score.

Abstract: An image processing apparatus, includes an image classifier configured to determine whether an input image is a low-quality image or a high-quality image; and an image evaluator configured to determine a first predetermined number of clearest images from a plurality of low-quality images determined by the image classifier.

Abstract: A method of detecting a target includes determining a quality type of a target image captured using a camera, determining a convolutional neural network of a quality type corresponding to the quality type of the target image in a database comprising convolutional neural networks, determining a detection value of the target image based on the convolutional neural network of the corresponding quality type, and determining whether a target in the target image is a true target based on the detection value of the target image.