单晶硅生产过程中，引晶工艺产生的缺陷严重影响产品质量，传统的基于视觉的缺陷检测方法在检测引晶图像中的凸点小目标时，存在检测速度慢、参数量大、难以部署在嵌入式终端等不足。为此，提出了一种改进的YOLOv8目标检测模型，引入了ContextGuided模块，提高了模型的推理效率；在特征融合网络中引入更为高效的DySample，优化了特征融合的效率和深度；采用轻量级网络结构，减少了模型的复杂度和计算量，使其适应计算资源有限的终端设备。在工业数据集上进行了训练和测试，实验结果表明，对凸点小目标的检测更加准确，mAP （mean average precision）达到97.7%，在精确率上相对于YOLOv8n提升了11.6%，同时参数量减少31.9%，方便部署在嵌入式终端。

During the production of monocrystalline silicon, defects generated during the crystal pulling process are recognized to severely impact product quality. Traditional visual-based defect detection methods, when applied to the detection of small protrusions in crystal pulling images, are confronted with challenges such as slow detection speeds, large parameter volumes, and difficulties in deployment on embedded terminals. In response to these challenges, an improved YOLOv8 object detection model was proposed incorporating a ContextGuided module to enhance the inference efficiency of the model. An efficient DySample was introduced into the feature fusion network to optimize the efficiency and depth of feature fusion. A lightweight network structure was adopted to reduce the complexity and computational demands of the model, making it suitable for devices with limited computing resources. The model has been trained and tested on an industrial dataset, demonstrating a more accurate detection of small protrusions with a mean average precision(mAP) of 97.7%. Compared to YOLOv8n, it exhibits an increase of 11.6% in precision and a reduction in parameter volume by 31.9%, facilitating its deployment on embedded terminals.