Research on Upper and Lower Body Pedestrian Detection in Lawn Based on YOLO-CGO Environment
There are some problems in lawn environment pedestrian detection model,such as low recognition rate,large model size,multiple parameters,and slow recognition speed,which make it difficult to deploy to robot platforms with limited computing power.A more lightweight with high-precision YOLO-CGO model depending on YOLOv5s is proposed to solve the above problems.First,the feature extraction network of the model was reset using the lightweight network MobileNetv3,reducing the number of model parameters and improving detection speed.Then improve the C3 module of the neck network by combining CA(Coordinate Attention)attention module.In the end replacing convolution-al layers of the neck network with GSConv convolutional layers,and the last convolution layer was replaced by the ODConv convolution layer reduces the complexity of the model while maintaining accuracy.The experimental results show that the YOLO-CGO model designed in this paper on the self-built dataset reduces the parameter count by 38%,model volume by 38%,and computational load GFLOPS by 50%com-pared to the original model,achieving significant lightweighting;And compared with the original model,the model proposed in this article is superior in map@0.5 Up by 1 percentage point map@0.5 Increase by 1.7 percentage points above 0.95.This study indicates that the YOLO-CGO model proposed in the article can achieve excellent accuracy in extremely lightweight situations,providing a practical and feasible ap-proach for the automation and intelligence of lawn mowing robots with limited computing resources.
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