【摘要】 准确的缺陷检测对保证产品质量和设备可靠性起着至关重要的作用。小目标检测由于特征表征弱和背景干扰大而面临着独特的挑战。为了解决这些问题，将3个关键创新纳入YOLOv8框架：使用GhostNet卷积进行轻量级和高效的特征提取，增加P2检测层以增强小目标检测能力，以及集成三重注意力机制以捕获全面的空间和通道依赖关系。这些改进共同优化了小物体的检测性能，同时降低了计算复杂度。实验结果表明，增强模型的平均精度（mAP@0.5）为97.46%,平均精度（mAP@0.5∶0.95）为61.84%,与基线YOLOv8模型相比，性能分别提高了1.9%和3.2%。此外，该模型实现了158帧·s^-1的帧率，在保持实时检测能力的同时减少了50%的参数计数，进一步强调了其在复杂场景下小目标检测的效率和适用性。更多还原

【Abstract】 Accurate defect detection plays a critical role in ensuring product quality and equipment reliability. Small-object detection poses unique challenges due to weak feature representation and significant background interference. To address these issues,this study incorporates three key innovations into the YOLOv8 framework: the use of Ghost Net convolution for lightweight and efficient feature extraction,the addition of a P2 detection layer to enhance small-object detection capabilities,and the integration of the Triplet Attention mechanism to capture comprehensive spatial and channel dependencies. These improvements collectively optimize detection performance for small objects while reducing computational complexity. Experimental results demonstrate that the enhanced model achieves a mean average precision( m AP@ 0. 5) of 97. 46% and a m AP @ 0. 5 ∶ 0. 95 of 61. 84%,representing a performance improvement of 1. 9% and 3. 2%,respectively,compared to the baseline YOLOv8 model.Additionally,the model achieves a frame rate of 158 FPS,maintaining real-time detection capabilities while reducing the parameter count by 50%,further underscoring its efficiency and suitability for smallobject detection in complex scenarios.更多还原