Research On Technology For Insect Imagge Recognition Based On Deep Learning

Recognition of insect images is an important application of artificial intelligence.The technology can emancipate insect taxonomists from tedious tasks of species identification,and greatly satisfy the urgent demands of insect species identification in various applications.Most existing insect recognition systems resort to traditional pattern recognition techniques,which rely on manual segmentation of the target region,and the classification accuracy is also limited.Therefore,this thesis implements the deep learning based insect recognition technology and builds a practical recognition system.The main contributions are summarized as follows.1.A method of insect object classification based on deep neural network is implemented,and the basic insect recognition system is built.The user of the system can segment the region of insect object in the image and submit it to the recognizer for species identification.Meanwhile,we exploit the strategies of data augmentation and model fusion to further enhance its performance.2.A deep neural network based object detection technique for insect object location is implemented.Through experimental evaluation of several mainstream deep object detection methods,we selected the most suitable model for this purpose and improved its performance on small object insects.The method is applied to the insect recognition system to improve the operability for users.3.By modification on the classification and detection networks,we propose a classification method and a detection method for unknown classes,both of which can recognize new insect species not presented in the training database.Preliminary experimental results show that the methods can effectively discover unknown new species without decreasing the recognition accuracy on known species.