Image Recognition Method for Corn Northern Leaf Blight of Unmanned Aerial Vehicles Based on Deep Learning
DOI:
https://doi.org/10.54097/eh1h6d53Keywords:
EMA Attention Mechanism; UAV image; YOLOv8-BiFPN; Corn Large Spot Disease.Abstract
Aiming at the problems of high complexity, false detection and missing detection of maize big spot based on UAV image, an improved algorithm for maize big spot detection was proposed. The algorithm is based on EMA and the improved YOLOv8-BiFPN feature pyramid network. By adding the efficient multi-scale EMA attention mechanism module, the capturing ability of detail information is improved, thus enhancing the feature extraction ability of the model. The YOLOv8 structure is improved into YOLOV8-BIFPN feature pyramid network by integrating BiFPN structure, which can extract context information more efficiently. By introducing WloU loss function, low quality samples in training data are filtered effectively, and the generalization ability of the model is improved. In this paper, the accuracy P was increased by 2.7%, the recall rate R was increased by 4.7%, the average accuracy mAP50 was increased by 3.1%, and the model size was reduced by 2.78M. Compared with other YOLO algorithms, the proposed method has significant advantages in the detection of corn big spot disease.
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