基于视觉的水面背景下目标检测与跟踪算法.

In order to solve the clean boat problem of detecting and tracking the small volume or occlusion of floater in the background of complex water surface, a target detection and tracking method under the background of surface based on visual algorithm was proposed. YOLO improved multiple granularity characteristics fusion method was used to make the model in the final test when the extracted feature vector to consider more features of the underlying. And K neighborhood search interested area module was introduced. The module combined with both short-term and long-short term memory(LSTM) made up the temporal correlation of the convolutional neural network with poor defects, according to the target current frame semantic features and motion prediction target location in the next frame, which makes the feature extraction of the target faster and effectively removes the interference of complex background. The results show that the average success rate, average accuracy and average speed of the algorithm are 57. 1%, 71. 1% and 45. 4 frame/ s, respectively. It is concluded that the algorithm can better solve the problem that the detected target is too small and improve the tracking performance when the tracking target is obscured. [ABSTRACT FROM AUTHOR]