An accurate detection algorithm for time backtracked projectile-induced water columns based on the improved YOLO network

During a sea firing training,the intelligent detection of projectile-induced water column targets in a firing video is the prerequisite for and critical to the automatic calculation of miss distance,while the correct and precise calculation of miss distance is directly affected by the accuracy,false alarm rate and time delay of detection.After analyzing the characteristics of projectile-induced water columns,an accurate detection algorithm for time backtracked projectile-induced water columns based on the improved you only look once(YOLO)network is put forward.The capability and accuracy of detecting projectileinduced water column targets with the conventional YOLO network are improved by optimizing the anchor box through K-means clustering and embedding the squeeze and excitation(SE)attention module.The detection area is limited by adopting a sea-sky line detection algorithm based on gray level co-occurrence matrix(GLCM),so as to effectively eliminate such disturbances as ocean waves and ship wakes,and lower the false alarm rate of projectile-induced water column detection.The improved algorithm increases the mAP50 of water column detection by 30.3%.On the basis of correct detection,a time backtracking algorithm is designed with mean shift to track images containing projectile-induced water column in reverse time sequence.It accurately detects a projectile-induced water column at the time of its initial appearance as well as its pixel position in images,and considerably reduces detection delay,so as to provide the support for the automatic,accurate,and real-time calculation of miss distance.

3D pulmonary vessel segmentation based on improved residual attention u-net

Automatic segmentation of pulmonary vessels is a fundamental and essential task for the diagnosis of various pulmonary vessels diseases.The accuracy of segmentation is suffering from the complex vascular structure.In this paper,an Improved Residual Attention U-Net(IRAU-Net)aiming to segment pulmonary vessel in 3D is proposed.To extract more vessel structure information,the Squeeze and Excitation(SE)block is embedded in the down sampling stage.And in the up sampling stage,the global attention module(GAM)is used to capture target features in both high and low levels.These two stages are connected by Atrous Spatial Pyramid Pooling(ASPP)which can sample in various receptive fields with a low computational cost.By the evaluation experiment,the better performance of IRAU-Net on the segmentation of terminal vessel is indicated.It is expected to provide robust support for clinical diagnosis and treatment.