SEGMENTATION EFFECT ASSESSMENT METHOD AND APPARATUS BASED ON DEEP LEARNING, AND DEVICE AND MEDIUM

摘要

The present application relates to artificial intelligence and deep learning. Disclosed are a segmentation effect assessment method and apparatus based on deep learning, and a device and a storage medium. The method comprises: acquiring a vehicle image, and acquiring segmented images according to the vehicle image and a pre-trained component segmentation model; creating training labels according to features of the segmented images, wherein the training labels comprise good segmentation effect labels indicating that the segmented images have the features of conforming to a preset shape and/or being uniform in color, and bad segmentation effect labels indicating that the segmented images have the features of having dots and/or color crossover; labeling the segmented images according to the training labels, so as to obtain labeled images; acquiring a target segmentation effect assessment model according to a constructed deep learning model and the labeled images; and acquiring an image to be tested, and acquiring an assessment result according to the image to be tested and the target segmentation effect assessment model. By means of the segmentation effect assessment method proposed in the present application, the segmentation effect of a component segmentation model can be assessed, and an image that fails to be segmented by the segmentation model can be filtered out.