An improved multi-scale fusion identification method for electric vehicle charging ports
To address the low accuracy in electric vehicle charging port recognition due to the complex background during the operation of unmanned automatic charging stations,this paper proposes a charging port recognition method for electric vehicles based on the improved YOLOv5 algorithm.First,the method incorporates a weighted bidirectional feature pyramid structure to enhance information fusion capabilities between different levels.Second,it introduces GhostConv,a depthwise separable convolution from the GhostNet network structure,replacing the ordinary convolution layers in the original feature extraction network,reducing the computational overhead of the model.The main network employs the SENet structure to increase the receptive field information,enhancing the model's ability to extract charging port features.Meanwhile,the loss function of the model is improved by introducing the EIoU loss function to replace the original CIoU loss function,enhancing the accuracy of bounding box regression.Our experimental results demonstrate the improved model,compared to the original YOLOv5 algorithm,reduces the model size by 6.94 MB and achieves a detection accuracy of 94.75%on a self-made,diverse dataset of electric vehicle charging ports.Furthermore,compared to the mainstream detection algorithms,it delivers superior detection accuracy and speed,making it suitable for target detection of electric vehicle charging ports in complex background environments.
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