Research On Object Detection Algorithm And Video Analysis Based On YOLOv5

Object detection is a technology to identify and locate the object of interest in the image.The performance of object detection directly determines the performance of target tracking,action recognition and other visual tasks.So object detection is very important and indispensable in the field of computer vision.The object detection algorithm based on deep learning uses the CNN(Convolutional Neural Network)to extract the image features step by step,independent learning and acquisition of characteristic information improves the accuracy of detection and shortens the detection time.Because of its high precision and real-time performance,object detection algorithms based on deep learning are widely used in various fields.This paper studies video content analysis based on YOLOv5 object detection algorithm,and the research contents are as follows:(1)Aiming at the problem that the current deep learning-based target detection algorithm has many parameters and low speed,which leads to low real-time detection,an improved target detection network model,CGS-Ghost YOLO,based on YOLOv5,is proposed.Firstly,the Stem Block module is added after the input end of the network for downsampling,which can improve the generalization ability of the network while maintaining strong feature expression ability.And a CGS-Ghost convolutional module is proposed,which can reduce network parameters to a certain extent while maintaining the feature extraction effect.In view of the problems of low feature expression ability and weak location information contained in features leading to low detection accuracy,a CA coordinate attention mechanism is incorporated into the network Bottleneck CSP structure and in front of the SPPF module to enhance the network library and library for extracting the location and semantic information of features.Then,Bi-FPN bidirectional cross-scale link weighted fusion network is used in the neck network,so that the whole feature extraction network can fuse more features,and the detection results are more accurate.(2)Based on the CGS-Ghost YOLO network model and combined with the Rep Ghost module,the optimization is carried out to maintain the network performance and further reduce the network parameters.The pyramid pool layer of empty space is used to expand the sensitivity field and enhance the feature extraction information.In terms of the Loss function,SIo U Loss is used as the frame regression loss.Considering the difference of Angle,distance and shape between the predicted box and the real box,the regression operation is more efficient,the speed of model training is accelerated,and the accuracy of model detection is improved.For the video analysis task based on object detection,the improved CGS-Rep Ghost YOLO model is accelerated by Tensor RT and deployed to the embedded device Jetson Nano.Combined with the Deep Stream intelligent video analysis process,multi-model combined detection and multi-channel video simultaneous reasoning are realized.Intelligent video analysis and other functions.