On Lightweight Road Edge Perception Algorithm for Auton-omous Path Finding of Street Sweeping Vehicles
Driverless garbage sweepers often achieve autonomous path selection through curb detection.Although the traditional deep learning method has a good recognition effect,the amount of computa-tion is too large and does not meet the practical application requirements.Therefore,an improved GDFE-YOLO algorithm based on YOLOv5 lightweight was proposed.Ghostconv and C3Ghost are used to replace the backbone of the original network model,and then the deformable convolution DC-Nv2 is introduced to replace the traditional Conv convolution module in the Neck part,and finally the loss function is replaced by Focal-EIOU Loss.Experimental results show that the GDFE-YOLO algo-rithm reduces the number of parameters,the amount of computation and the size of the model by 16.2%,15%and 19.4%,respectively,and the detection speed is increased by 12%,and the average accuracy of curb recognition is 96.1%.Compared with the YOLOv5s algorithm,the recognition accura-cy of GDFE-YOLO is only reduced by 0.9%,and based on the linear fitting detection strategy of the overall curb,the accuracy of a single point has little impact,so the proposed algorithm can realize the lightweight curb detection requirements of sweepers.
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