Research On Multi-Object Detection Algorithm By Convolution Neural Network Based On Context Information

Object detection is a hot research direction in the field of image recognition and computer vision.In many fields,it has a wide range of uses.In the field of automatic driving vehicle,it is reasonable to avoid pedestrians and vehicles through object detection.In the field of image processing,the tasks of image classification,semantic segmentation and scene understanding are based on the object detection.Therefore,the research of object detection has great significance and wide development prospects.At present,although the object detection task in a single frame image has entered formal,a part of detection algorithm has reached real-time processing,but because it is not specifically designed for video detection,so the detection method of single frame image is directly applied to video object detection tasks still exist many problems.Therefore,two aspects of multi-object detection in video are studied in this paper,in order to adapt to the diversity of multi objects,the object detection algorithm based on convolution neural network is improved,in order to improve the accuracy of object detection,multi object detection is fused with context information.In terms of object detection based on convolutional neural network,it is mainly improving extraction of region proposal and detecting boxes suppression algorithms.First,two methods to extract region proposal are introduced,and their advantages and disadvantages are analyzed.The existing methods to extract region proposal have large amount of computation and overlap area,and cannot adapt well to the morphological diversity of objects in multi object detection tasks.In this paper,a multi scale feature extraction region proposal algorithm is proposed,which pays attention to the semantic information of objects on multiple levels,and reduces the probability of detecting small objects being missed by multi objects.In the detection boxes suppression algorithm,in view of the fact that the existing suppression algorithms can not effectively judge adjacent objects,this paper proposes a non maximum suppression algorithm based on the central point to suppress the redundant window by determining the center point distance of the detection boxes to reduce theprobability of the leakage detection.In the aspect of multi object detection in video,this paper first introduces the problem of multi object detection in video,because the video sequence is composed of continuous multi frame pictures,the multi object detection based on video sequence has a great similarity with the multi object detection based on single frame picture,but the detection algorithm of single frame image does not consider the context information of video sequences,in video sequence,it has a wealth of relevant information can be used for the same object.Therefore,this paper puts the context information into the convolution neural network multi object detection,and uses the correlation of adjacent frames to improve the accuracy of object detection in the current frame.This algorithm can improve the accuracy of multi object detection in the video.But because of handling the more complex problems,the complexity of algorithm will increase.