The Object Detection Base On Deep Learning Convolution Neural Network

Object detection is an important research topic in the field of computer vision.The main purpose of object detection is to locate and identify objects of interest from a static picture.In the traditional method,the object is located first,then the feature is extracted,and then the classifier is introduced into the classifier.The feature extraction and classification of targets are carried out separately.In complex environment,it is not only necessary for experienced researchers to manually extract features,but also time-consuming.It makes a huge amount of work in the process of target extraction and does not get good results.With the rapidly increasing of technology,the improvement of machine performance and the arrival of the era of big data,target detection based on the structure of convolution neural network is reintroduced.It uses the convolution operation of input image to extract features with characteristic information directly,and adjusts parameters in convolution neural network through forward propagation and back propagation.Further more,convolution neural network does not require the original image to have a fixed size when extracting feature,so that the extracted feature information is more complete and accurate.The object feature extraction and classification based on deep learning convolution neural network object detection is much better than traditional methods that it can be loaded into a network and end to end processing,and finally get the more representative features.At the same time,convolution has the characteristics of weight sharing and sparse connection.It greatly reduces both of parameter numbers and the complexity of computation,and make the algorithm more efficient.In simple terms,the main is shown below:(1)This paper first describes the background of this research,analyzes the current research status of target learning algorithm of deep learning convolution neural network at home and abroad,introduces some of the important research of traditional algorithm,and some operations on picture processing.(2)The structure of neural network and its basic knowledge are deeply understood,and forward propagation and reverse propagation in neural network are described.It also explains the handling of over-fitting phenomenon in deep neural network,and introduces several commonly used optimization algorithms in deep learning.Finally,the basic content and main characteristics of convolution neural network are introduced in detail.(3)This paper introduces the structure of target detection based on deep learning convolution neural network in the text,and describes some methods of how to extract the target feature,the generation of region proposal box,ROI pool layer and multi-task loss.Finally,by improving the network structure extracted from the features,the characteristics of the information are obtained to improve the accuracy of the object detection.In a complex background,when the data is sufficient,with the structure of the network deeper and deeper,the more accurate features can be extracted to improve the accuracy of the object detection,however the training complexity is increasing by the number of layers,because there will be a gradient elimination and a gradient explosion.The residual neural network we use can suppress these problems well,so that we can get more characteristic information characteristics and enhance the accuracy of target detection.