Sugarcane Bud Detection Method Based onYOLOv3-CSE Network

Abstract

It is specified in agronomic requirements of sugarcane sowing that sugarcane buds should be placed toward the walls on bothsides of the sowing ditch, while the traditional detection model of small sugarcane bud targets cannot meet the requirementsof intelligent directional seeding machine for sugarcane bud identification due to such shortcomings as low accuracy, lowrecognition speed, and low training speed. To this end, a network model targeting sugarcane buds, called YOLOv3-CSE wasproposed in this paper. On the basis of analyzing the advantages and disadvantages of the YOLOv3 network, the originalYOLOv3 network was improved to achieve accurate and rapid identification of small and medium-sized targets in sugarcanebuds. Besides, to further enhance the detection ability of the model for small object regions such as sugarcane buds, the originalDarkNet-53 network structure and the complete intersection over union (CIoU) bounding box regression loss function wereimproved to make the real box regression more stable, thus avoiding IoU divergence in the training process and ameliorating theregression effect on sugarcane bud identification. Mosaic data augmentation method was applied to enrich the data diversity,so as to solve the inadequate generalization ability during small dataset training. Finally, SE-ResNet module was embedded toincrease the ability of network model to identify sugarcane bud features. The test results of the YOLOv3-CSE network andthe original YOLOv3 network indicated that the precision and mean average precision (mAP) of the YOLOv3-CSE networkwere 96.93% and 95.87%, which were 5.66% and 4.95%, respectively, higher than those of the original YOLOv3 network.Compared with other object detection models with the same dataset, the YOLO v3-CSE network proposed in this paper boastsstronger robustness, better instantaneity, higher precision and higher detection velocity in identifying small objects of sugarcanebuds. In addition, it can rapidly identify the sugarcane buds, providing a technical guarantee for the application of the intelligentdirectional seeding machine for sugarcane seeds.