Improved road depression detection algorithm for YOLOv8
To address the slow detection speed in the existing road depression detection algorithms,which are hardly applicable on vehicle-mounted mobile devices,this paper proposes a lightweight road depression detection algorithm,YOLOv8-CAG.First,the ordinary convolution after the second layer of the backbone network of YOLOv8 is replaced with the Ghost Conv,which effectively reduces the parameter number of the model by an inexpensive linear transformation.Second,the CA attention mechanism is introduced into the C2f module in the neck,reducing the number of parameters and floating-point operations of the overall model,offsetting the impacts of irrelevant features while strengthening the feature extraction capability.Finally,the C2f-GS module is utilized in YOLOv8 to decrease the complexity of the network structure and further improve the detection accuracy.The practicality of our improved algorithm is verified by comparing its performance with other algorithms.Experimental results show,on the dataset of road depression,our algorithm improves the performance by 1％compared with that of the original one.And the number of model parameters and the computation are down by 16％and 11％respectively.
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