Improved Urban Plant Detection Algorithm Based on YOLOv7-Tiny
Detection and recognition of urban roadside plants is a key technology for intelligent sprinkler vehicles.A modified YOLOv7 Tiny plant detection algorithm is proposed to address the issues of small target missed detection and occlusion in roadside vegetation image detec-tion.When creating a dataset,the original dataset is obtained using camera realistic shooting and image crawler crawling methods,manually annotated using LabelImg,and the dataset is expanded using Mosaic data augmentation methods.To achieve both accuracy and high detection speed,the YOLOv7 Tiny network is first used as the baseline,and a parameter free SimAM attention mechanism is introduced in the Head part of the network to focus on more important feature information without increasing model complexity;Then,in the Head section of the net-work,ACmix is replaced with some traditional convolutions to achieve more efficient feature fusion;Finally,in the algorithm,SIOU is used to replace the CIOU of the original YOLOv7 Tiny network model to optimize the loss function,reducing the degree of freedom of the loss function and improving network robustness.The experiment shows that the average accuracy of the improved algorithm on the test set is average mAP@50:95 reaches 67.2%,which is 3.1%higher than the YOLOv7 Tiny algorithm.It has high detection accuracy while ensuring the lightweight of the model,and can meet the accuracy and speed requirements of lightweight plant detection for intelligent sprinklers..
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