Research on obstacle detection method of mowing robot working environment based on improved YOLOv5

In order to realize the fast and accurate positioning and identification of obstacles in the working environment by lawn mowing robot with limited computing resources,an obstacle detection method of mowing robot based on improved YOLOv5 sdeep learning model with filter pruning is proposed.Firstly,the YOLOv5 model uses a layered residual structure to represent multi-scale features with finer granularity,and the network receptive fields are added.In addition,SE module is added to the tail of the residual block to recalibrate the feature map.Secondly,the filter pruning is performed for the improved algorithm.Finally,the relevant data sets were established for common obstacles in the working environment of lawn mowing robot,and the improved YOLOv5 safter pruning was used as a deep learning model for detection.Experimental results show that the size of the improved YOLOv5 model is reduced by 18.8%,and the mAPis increased by 0.1%.After pruning the improved YOLOv5 model,the computational amount and the model size are reduced by 36.6%,33.3%,and 1.9ms,respectively,compared with the original model.After pruning,the mAPof the final model is 1.3%,9.5%,5.8% and 22.1% higher than that of YOLOv4,YOLOV4-tiny,YOLOv3 and YOLOV3-tiny,respectively.