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| Defect detection method for strip alloy functional materials based on improved YOLOv8 |
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Abstract In order to solve the problems of missed detection, false detection, and slow detection speed in the defect detection of strip alloy functional materials, a defect detection algorithm for strip alloy functional materials based on improved YOLOv8 is proposed. In order to fully integrate the multi-scale features extracted by the model backbone network, a multi-scale feature encoder (MFE) module is first designed, and a multiscal feature affregation-diffusion (MFAD) structure is constructed at the neck. The unique diffusion mechanism is used to diffuse features with rich contextual information to all scales. Then, a shared parameter task dynamic alignment detection head (TDADH) is designed at the head of the model. Through convolution parameter sharing and task alignment mechanisms, the model complexity is reduced while the detection accuracy is improved. Finally, a perceptual attention spatial pyramid pooling (PASPP) module is designed to enhance the feature expression ability of the model using the explicit dynamic selection mechanism of attention mechanism. Experimental results indicate that the method proposed attains a mean average precision (PmAP50) of 90.1% on the alloy functional material dataset. It boasts a parameter count of 2.543×106 and a detection speed of 232FPS, outperforming leading deep detection algorithms. Moreover, it achieves top performance on the GC10-DET and PASCAL VOC2012 datasets, demonstrating strong generalizability ability.
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Received: 15 July 2024
Published: 19 March 2025
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| Fund:National Natural Science Foundation Project;Zhejiang Natural Science Foundation Project;Jiaxing Science and Technology Plan Fund Project |
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2025, Vol. 46 
