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烟丝异物剔除是卷烟生产加工的重要环节。为进一步提高烟丝异物识别精度,提出基于改进YOLOv8的烟丝异物检测网络(TSFODNet)。首先,TSFODNet使用MCIoU_Loss替换CIoU_Loss以解决模型在训练过程中定位损失函数性能退化问题;然后,在模型主干部分嵌入多感受野特征自适应融合模块(MRFA),将具有不同感受野的特征图进行自适应加强融合进而提高模型对烟丝异物的识别精度;再使用C2f_GhostNetv2和C3_GhostNetv2构建颈部特征增强融合模块(NFEF)以达到保留更多深层有效特征信息的目的;最后,使用所建立的烟丝异物数据集进行实验验证。实验结果表明,相较于多种目标检测网络,TSFODNet取得了97.9%的最好验证精度和97.6%的最好测试精度,且图像识别速度达到91帧/s,可为高精度烟丝异物实时检测提供技术支撑。

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