DROPPING EAR DETECTION METHOD FOR CORN HARVERSTER BASED ON IMPROVED Mask-RCNN

Abstract

In order to quickly and accurately identify the corn ears lost during the corn harvesting process, a corn ear loss detection method based on the improved Mask-RCNN model was proposed. The lost corn ears in the field were taken as research objects, the images of the lost corn ears were collected and the fallen ears data set was established. The size ratio of the Anchor Box of the area recommendation network was changed by changing the K-means algorithm to reduce the influence of artificial setting intervention. The group convolution was introduced into the residual unit and the channel dimension was divided into 3 equal parts to reduce the model parameters in the basic feature extraction network ResNet. A Convolutional Block Attention Module (CBAM) was introduced to improve the accuracy of the model in the last layer of the ResNet network. Results showed that the average target recognition accuracy of the method on the test set in this study was 94.3%, which was better than that of the previous model, and the average time to recognize a single image was 0.320 s. The proposed method could detect the lost corn ears during the harvesting process under the complicated background, and provide a reference for the corn ear loss detection of the corn harvester.