A Corner Domain Recognition Method of Lidar Detection Images for High Maneuvering Target Detection
The high-speed movement and rapid transformation of motion modes can easily cause target blurring,making it difficult to accurately determine the contour,shape,and position of the target.To this end,a corner do-main recognition method for lidar detection images targeting high maneuverability target detection is proposed.First-ly,by detecting abnormal pixel distance within the neighborhood range,noise and abnormal signals in the LiDAR detection image are removed;Then,L-R algorithm is used to solve the image blur problem caused by high-speed moving targets;Finally,the image is segmented into different angle domains using an adaptive corner domain parti-tioning method based on mutual information,and high-precision target recognition is performed in each angle do-main through convolutional neural networks to achieve high maneuverability target lidar detection.The experimental results show that this method can effectively remove the blurring phenomenon of lidar detection images caused by high maneuvering targets;Compared to other traditional methods,this method has higher target recognition rate and overall average accuracy,lower average time consumption for single image recognition,and has good recognition performance.
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