Research On Object Detection Algorithm Based On Improved Faster R-CNN

In recent years,the important role of computer vision in daily life has become increasingly prominent.Object detection,as one of the basic work of computer vision,has been widely used.It can not only identify object but also interpret pictures,videos and other materials.It can be said that object detection can be seen in all aspects of society.Object detection is to solve the problem of object classification and positioning.However,due to the various shapes of objects,the interference of background and light,as well as the phenomenon of mutual occlusion between objects,it is of great significance to strive to enhance the accuracy of object detection.When object detection is applied in the area of autonomous driving that require high safety,as well as in the medical field to assist doctors in medical diagnosis,its accuracy has also attracted widespread attention.Therefore,it is an inevitable trend to further improve the accuracy of target detection.The traditional method and the deep learning method are two main methods to solve the problem of object detection.For traditional methods,most algorithms are designed by manual features.Although traditional object detection algorithms are relatively simple,they are only suitable for situations with single object and obvious features.However,objects in real life are various,and the background is changeable,so it is very difficult to find the common features of many objects to achieve object detection.As for deep learning methods,two-stage object detection algorithms and one-stage object detection algorithms are two main mehods.The two-stage algorithm first generates candidate boxes on the image,and then classifies and regresses the candidate boxes,while the one-stage algorithm directly transforms the classification and location of the object into a regression problem.Because the two types of algorithms are implemented in different ways,their performance is also different.The two-stage algorithm has high accuracy,while the one-stage algorithm is fast.In this paper,the representative Faster region-based Convolutional Neural Network in the two-stage object detection algorithm is improved.A new network architecture,the object detection algorithm based on improved Faster R-CNN,is proposed to make it have better object detection effect.The main improvements of this paper are as follows:(1)To make the setting of the learning rate more reasonable and find a learning rate that is more suitable for network training,this article adopts the whale optimization algorithm to optimize the learning rate.The training process is divided into two stages to find the optimal learning rate.In the first stage,the whale optimization algorithm is used to find the optimal value of the high learning rate in the interval(0.0002,0.002)and accelerate the training convergence speed.When the training reaches the specified number of iterations,the training enters the second stage.In the second stage,the whale optimization algorithm is used to find the optimal value of the low learning rate in the interval(0.00002,0.0002].Until the training reaches the final specified number of iterations,training convergence is realized and better training results is achieved.(2)To enhance the overall performance of the loss function,this paper optimizes the loss function and adds two weighting factors,which are used to adjust the weight of the classification loss and the regression loss respectively.At the same time,in order to make the setting of these two weighting factors more reasonable,this paper uses the whale optimization algorithm to optimize the two weighting factors.This paper has conducted experiments on two datasets,and the results prove that the detection accuracy of the algorithm proposed in this paper has obtained good accuracy in the field of object detection.Moreover,compared with several classic algorithms,the algorithm in this paper obtains better object detection accuracy.Therefore,it can be proved that the algorithm in this paper has universality and application value.In order to verify whether the improvement proposed in this paper is valuable,experiments are carried out on the three datasets of Pascal VOC 2007,Pascal VOC 2012 and MS COCO.The results prove that the algorithm of this paper is valuable in the field of object detection.Moreover,compared with several classical algorithms,the algorithm in this paper also achieves better results.Therefore,it can be proved that the algorithm in this paper has universality and application value.