Research On Apple Target Recognition And Location Algorithm Based On Deep Learning

In china,apple is the fruit with the largest planting area,the highest yield and the most important economic status.Up to now,apple picking is still dominated by manual picking.During the ripening period,apples has the large amount of picking and the high labor intensity.Moreover the limitation of daily picking and the high labor cost,it will affect the quality of the apples if it cannot be picked in time.Fast automatic apple picking can greatly improve the efficiency of apple picking and reduce the labor cost.The object identification and positioning of apple is the key technology of fast automatic apple picking,which is of great significance to realize fast automatic apple picking.Aiming at the application requirements of apple picking robots in natural environment,combined with image processing technology,an object recognition and orientation algorithm for apple based on deep learning was proposed.The main research contents are as follows:(1)Research on image processing method based on apple object recognition.In order to improve the recognition rate of the apple,11080 images of apples in the natural environment were collected.The differences between RGB,HSI and Lab were analyzed and compared,and the most suitable RGB color space was selected.In addition,the effect filtering operations on the image denoising was analyzed and compared.Finally,the LabelImg software was used to calibrate the position of the processed images and the training set and test set were established.(2)Research on object feature extraction of apple based on MobileNetV1 Network.Through introducing the process of the convolutional neural network and analyzing the advantages and disadvantages of VGG network,ResNet residual network and MobileNetV1 network,the MobileNetV1 network which with higher performance and less calculation parameters was selected to extract the characteristics of apple images and improve the speed of detection for apple.(3)Research on multi-target and multi-scale recognition of apple based on improved SSD algorithm.Aiming at the problem of the multi-objective multi-scale recognition of the apple in the natural environment,based on the comparison and analysis between Faster R-CNN algorithm and SSD algorithm,the MobileNetV1,FPN network as well as different proportions of anchor frame length-width ratio were finally adopted to improve the SSD algorithm,so as to complete the multi-target multi-scale detection of apples and obtain the apple picking center.The experimental results showed that(Average Precision)of apple was 0.925 and the detection speed was 0.072 s/web.(4)Research on three-dimensional spatial localization based on apple picking center.The matlab software was used to calibrate the non-parallel binocular camera and obtain the internal and external parameters and distortion coefficients of the left and right cameras.And the stereo correction algorithm was adopted to keep the left and right images in the same horizontal position.In order to improve the accuracy of stereo matching,the affine transformation matrix was obtained by using SIFT feature point matching,NCC matching function and RANSAC calculation.The affine matrix was used to obtain the coordinates corresponding to the picking center in the left image in the right image.The three-dimensional space coordinates of apple fruit were obtained based on the principle of triangle ranging.The experimental results showed that the measurement distance and the actual distance error was within 15 mm.The experimental results showed that the algorithm used in the paper has better performance in recognition accuracy and recognition efficiency,which lays a foundation for further research on apple picking robot vision system.