目的 针对生鲜肉类包装前检环节中,附着于产品表面的毛发、塑料纤维等微小异物极易受肉质表面镜面反射及生理纹理特征混叠干扰,导致漏检率高的问题,提出一种基于改进YOLOv8n的轻量化在线检测方法M-YOLOv8。方法 首先,利用离线实例级Copy-Paste算法配合泊松融合,重点针对“异物附着”工况构建小目标增强数据集,模拟异物与肉质表面的光影融合特征,修正样本长尾分布偏置。其次,在主干网络C2f模块中嵌入CBAM注意力机制以抑制油脂反光并增强异物区域特征信噪比,并引入P2微尺度检测层与SAHI切片推理策略,补偿深层特征下采样带来的空域信息丢失。最后,采用CIoU损失函数优化细长线性附着物的边界框回归精度。结果 在自建鸡翅包装产线数据集上,该方法的AP@0.5达到91.8%,较原始模型提升4.3%,尤其是对直径小于2 mm的微小附着异物检出率显著提升。系统推理速度达98 FPS,满足高速产线实时节拍要求。结论 该方法有效兼顾了复杂背景下附着目标的检测精度与端侧推理速度,可为解决肉禽包装检测中的“长尾分布”与镜面干扰难题提供可靠技术参考。
Abstract
The work aims to propose a lightweight on-line detection method based on an improved YOLOv8n, named M-YOLOv8 to address the high miss-detection rate of tiny non-metallic foreign objects (such as hair and plastic fibers) attached to product surfaces in fresh meat automated packaging lines, which are highly susceptible to interference from specular reflections and complex physiological textures. Firstly, an offline instance-level Copy-Paste algorithm combined with Poisson blending was employed to construct a small-target augmented dataset. This process specifically targeted "foreign object attachment" scenarios by simulating the light-shadow fusion characteristics between objects and meat surfaces to correct long-tail distribution bias. Secondly, a CBAM (Convolutional Block Attention Module) was embedded into the C2f module of the backbone to suppress fat reflections and enhance the feature-to-noise ratio in target regions. A P2 micro-scale detection layer and the SAHI (Slicing Aided Hyper Inference) strategy were integrated to compensate for spatial information loss caused by deep feature downsampling. Finally, the CIoU loss function was utilized to optimize the bounding box regression accuracy for slender linear attachments. Experimental results on a self-built chicken wing packaging dataset showed that the AP@0.5 of the proposed method reached 91.8%, a 4.3% increase over the baseline model. Specifically, the detection rate for micro-objects with a diameter of less than 2 mm was significantly improved. The system inference speed reached 98 FPS, meeting the real-time requirements of high-speed production lines. The proposed method effectively balances the detection accuracy of attached targets under complex backgrounds with edge-side inference speed. It provides a reliable technical reference for addressing the challenges of "long-tail distribution" and specular interference in meat and poultry packaging detection.
关键词
包装检测 /
微小异物 /
YOLOv8n /
SAHI /
注意力机制 /
Copy-Paste
Key words
packaging detection /
micro-foreign objects /
YOLOv8n /
SAHI /
attention mechanism /
Copy-Paste
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基金
河北省自然科学基金（F2020203003）; 国家自然科学基金资助项目（61973264）
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