In order to solve the problem of low efficiency of massive capsules screening in laser inertial confinement fusion experiments, a rapid capsules screening method based on improved YOLO-V5 deep learning model was proposed. In this method, the capsules were imaged in different scene depths, and the images were spliced together to obtain the clear images; At the same time, the channel attention mechanism was introduced to enhance the feature extraction ability of the model, and the SE-YOLOV5s deep learning capsule surface defects recognition model is established, and the capsule defects are classified and evaluated according to the defect types to achieve the screening of massive capsules. The accuracy of capsule surface defect detection is 94.4%, with fifty capsule images (resolution 3072×4096) detected per second, providing a fast and accurate method for screening massive targets for laser inertial confinement fusion test.