Research On Fast Object Detection Algorithm Based On Deep Learning

With the popularization of video surveillance applications and the rapid development of computer science and technology,computer vision technology has been widely used.Vision-based object detection technology is a very important part in the field of computer vision,and many higher-level visual processing and analysis tasks are based on this.Object detection technology is widely used in face recognition,smart driving and other fields.The traditional object detection algorithm is based on artificial extraction features,the accuracy rate is easily affected by factors such as the acquisition conditions,and the efficiency is low and generalization is weak.In recent years,the object detection algorithm based on deep learning has far exceeded the traditional object detection algorithm in detection accuracy,and backed by big data to automatically learn and extract features,the effect is far better than the artificial design feature.Although deep learning is far more than traditional algorithms in many aspects,its disadvantages are also very obvious.It requires a large amount of training data,large model parameters,slow operation speed,and extremely high hardware requirements.For these reasons,deep learning algorithms are difficult to implement in real-time and are difficult to apply to embedded or small devices.This paper studies the speed of detection based on deep learning based on the premise of ensuring accuracy.The specific work of this article is as follows:First,the mainstream CNN network model is introduced in detail,and the model structure of the popular object detection algorithm based on deep learning and their advantages and disadvantages are analyzed and compared.Then,referring to the popular one-stage method,we design an end-to-end network model that directly classifies and regresses the target.And using the Tensor Flow platform to train the network,the network's m AP reaches 0.713,and the detection speed reaches 32.4 FPS on the GTX 1080.Finally,based on the convolutional neural network designed,the VGG-based network is replaced by the Res Net network with stronger feature extraction capability,which improves the model detection accuracy.The K-means++ method is used to cluster the aspect ratio of the target frame of the data set,and then the anchor frame of the network is improved,which improves the detection speed of the network.Next,the network model is compressed using a deconvolution,and the base network is reconstructed using a residual block of convolutional separation.As a result,the network's parameter amount and calculation amount are reduced to 1/8 to 1/9,and the network's m AP reaches 0.727.Detection speed reaches 47 FPS.The experimental results show that this paper has certain value to speed up the calculation speed of deep neural network and enhance its real-time performance.