Fine Grained Object Detection And Recognition And Its Application In Pest Assessment

In recent years,fine-grained object detection and recognition technology has developed rapidly,which is widely used in agricultural production,unmanned driving,terrain exploration,medical detection and other fields.Among them,Faster-Rcnn network algorithm and NTS-Net network algorithm have outstanding performance,which brings great convenience to human production and life.Due to the destruction of the natural environment,the occurrence of insect disasters is more and more frequent,which brings serious harm to crops.Therefore,for the common pest problems in agricultural production,this dissertation combines the Faster-Rcnn network algorithm and NTS-Net network algorithm to build an automatic detection mechanism of insects,and uses an intelligent way to identify the location and species of insects.Take pictures in different stages of crop growth,and then detect and identify the fine-grained insects in the collected images,so as to distinguish the species and location of insects,so as to solve the problem of insect pests in time.Effectively reduce labor costs and improve the efficiency of agricultural production.The main work of this disssertation is as follows.Firstly,this dissertation studies the Fast-Rcnn network in detail,and describes its basic structure and algorithm flow in detail,and then applies the algorithm to fine-grained object detection.In order to apply fine-grained object detection technology to the detection of insect position,a PASCAL VOC2007 format insect data set containing fifteen kinds of insects was constructed,and the experimental accuracy was 90.1%.Secondly,on the basis of insect position detection,this dissertation makes a more in-depth study of NTS-Net network,and then applies it to insect species recognition.In this paper,the principle and steps of using NTS-Net algorithm to identify insects are described in detail,and a clear flow chart is drawn.Then fifteen kinds of insects are identified,and the experimental accuracy is 80.2%.Finally,this dissertation studies some common pests in crops based on the experimental results,and introduces the harm of these insects to crops and the corresponding preventive measures in detail.In this way,in the critical period of crop growth,we can take photos of crops,and then detect and identify the insects in the photos,so as to take corresponding measures to control pests.