Auto spray painting robots can realize the automation of various types of auto lamp spraying. The auto lamp detection algorithm is the key technology of the robot. The Faster R-CNN model is selected to detect the lamp, and some improvements are made to the model to achieve better detection effect. To generate more multi-dimensional feature expressions for auto lamps, residual network ResNet101 and feature pyra-mid network (FPN) are used to extract the features of auto lamps. Compatible Intersection over Union (CIoU) is used as the bounding box regression loss function to provide moving direction and more accurate position information for the bounding box and accelerate the convergence speed of the model. Through the auto lamp detection experiment, the average recognition accuracy of the improved model is 98.96%, and the detection speed is 0.46s per image. The improved model can realize the effective recognition of auto lamp types and meet the requirements of real-time processing.
Aiming at the problems that the current RANSAC (Random Sample Consensus) algorithm has too large randomness and is susceptible to external point interference, which leads to the reduction of matching accuracy, an improved RANSAC algorithm combining feature matching confidence and grid clustering is proposed. Firstly, rough matching is carried out by the FLANN algorithm, and confidence analysis is carried out on the coarse matching point pairs, then expanding grid clustering around the high confidence point pairs. Multiple local optimal interior points are screened to optimize the global interior points and improve the matching accuracy of feature points. The experimental results show that the improved RANSAC in this paper increases the existence probability of interior points, avoids too many wrong feature matching affecting the model effect of the homography matrix, and improves the accuracy of feature matching.
In order to improve the professionalism and accuracy of machine translation in the field of notice to air navigation,a two-way machine translation model of notice to air navigation based on transfer learning and back translation is proposed to solve the problem of the parallel corpus of machine translation in the field of air notice.On the basis of training the machine translation of People's Daily,the parameters obtained from the training are transferred to the encoding end and the decoding end of the machine translation model of the partly back-translated announcements to initialize the parameters at both ends,and adjust the parameters at both ends through experiments.The two-way machine translation model of the notice of air travel is obtained.Experiments show that the introduction of the data enhancement strategy of transfer learning and partial back translation improves the robustness of the mod-el and improves the translation quality.The BLEU value of the translation evaluation index of the Chinese-to-English model has in-creased by 2.08%.The BLEU value of the translation evaluation index increased by 3.12%.
Scene texts with explicit semantic information in natural images can provide important clues to solve the corresponding computer vision problems. In the text, we usually focus on using multimodal content in the form of visual and text prompts to solve the task of fine-grained image classification and retrieval. In this paper, graph convolution network is used to perform multimodal reasoning, and the features of relationship enhancement are obtained by learning the common semantic space between salient objects and texts found in images. By obtaining a set of enhanced visual and textual functions, the proposed model is highly superior to the existing technologies in two different tasks (fine-grained classification and image retrieval in contextual texts).
To realize the automatic symptom recognition and classification of MR images and improve the accuracy and efficiency of the diagnosis of lumbar intervertebral disc herniation (LDH), a method for lumbar intervertebral disc recognition and disease classification is proposed in this paper. The method mainly includes three steps: preprocessing, target segmentation, and symptom classification. Preprocessing is performed by noise reduction and interference removal methods for blurred images. The contour poles are used to determine the four points of the tail vertebra in order to reduce the wrong segmentation of the tail vertebra. A classification method based on five judgment indicators is proposed, which effectively improves the stability of disease diagnosis. The example verifies that the algorithm can accurately complete the target segmentation and the accuracy of symptom classification reaches the standard of professional doctors, which proves that the method has good robustness.
