Blurry Flower Image Detection Based on SR-YOLOv8n-BCG
In order to meet the fast and accurate detection requirements for fuzzy flower images in complex environments,a composite model SR-YOLOv8n-BCG was proposed.This model effectively integrated the image reconstruction capability of SRGAN(super-resolution generative adversarial network)and the object detection capability of YOLOv8.Furthermore,the network structure was further improved to enhance accuracy and achieve lightweight design.Firstly,SR-YOLOv8n-BCG employed SRGAN to perform super-resolution processing on fuzzy flower images,enhancing the image quality input into model.Secondly,in the feature extraction network of YOLOv8n,a weighted bidirectional feature pyramid network(BiFPN)was used to replace the PAN-FPN module,effectively fusing multi-scale flower features and reducing the model size.Additionally,a coordinate attention(CA)mechanism was introduced to enhance the model's feature extraction ability.Finally,Ghost convolution was used to replace regular convolution,further improving detection accuracy and model lightweightness.Experimental results on a self-made dataset of five flower categories demonstrated that compared to SR-YOLOv8n,the SR-YOLOv8n-BCG model achieved an average precision improvement of 1.2 percentage points,reaching 95.4%with a 35.5%reduction in model size.The results above indicate that the proposed improved model effectively enhances the detection accuracy of fuzzy flower images and achieves lightweight design to suit lower-end devices.
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