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YOLOv5 demonstrates the potential of detection in regions with high canopy coverage by adjusting the network perceptual field size and enhancing the network feature extraction ability. so direct
training on it is prone to overfitting. To solve this problem, we add a dense connection module in the backbone part,. Overfitting is a modeling error that introduces bias to the model because it is too closely related to the data set. Overfitting makes the model relevant to its data set
only, and irrelevant to any other data sets. Some of the methods used to prevent overfitting include ensembling, data augmentation, data simplification, and cross-validation.