Camellia oleifera fruit detection based on UAV aerial photography and improved YOLOv5s
Aiming at the problem that the fruit of Camellia oleifera is small and occluding each other in UAV aerial images,an improved YOLOv5s model is proposed.Firstly,SPD-Conv is used to replace the pooling operation in the YOLOv5s model,so that the model can retain more fine-grained information during the down-sampling operation.Then,Coordinate Attention(CA)is introduced at the end of the neck network of YOLOv5s model to improve the robustness of the model to occluding targets.Additionally,the improved YOLOv5s model replaces the YOLOv5s CIOU bounding box loss function with the NWD(Normalized Gaussian Wasserstein)bounding box loss function to improve its ability to detect small Camellia oleifera fruits in drone aerial images.The precision,recall,F1 score,and mean average precision of the improved YOLOv5s model are 93.1％,90.5％,91.78％and 91.2％,respectively.Compared to the YOLOv5s model,the improved YOLOv5s model's mean average precision has increased by 3.6 percentage points.The experiments indicate that the improved YOLOv5s has stronger detection capabilities for smaller and occluded Camellia oleifera fruits in aerial images.This research can provide a reference for the estimation of Camellia oleifera fruit yield by using drones.
Camellia oleiferaUAV aerial photographyYOLOv5scoordinate attention mechanismbounding box loss function
万方数据
维普
