表征和测量单晶硅位错密度是检测晶体生长品质和研究位错形成机制的重要参量。基于位错腐蚀坑形貌差异大、背景复杂等非典型性特征，以及传统人工光学显微检测准确度不高、效率低下等问题，提出一种改进的YOLOv5算法检测单晶硅位错腐蚀坑密度分布。在原始的YOLOv5算法基础上引入注意力机制，优化网络结构，加强模型推算能力；进一步通过强化特征融合，提升网络检测精度；优化损失函数增强定位准确率，提升训练速度。实验结果表明：改进后的算法，对两种不同腐蚀液的单晶硅位错腐蚀坑检测精度分别达到93.52%和98.82%，检测平均精确率均值(mAP)能够达到96.17%，帧率(FPS)能够达到47 帧/s，满足实时检测的需求。

Characterization and measurement of monocrystalline-silicon dislocation density are the important parameters for detecting the crystal growth quality and studying the dislocation formation mechanism. Based on atypical characteristics of dislocation corrosion pits such as large differences in morphology and complex background, as well as low accuracy and efficiency of traditional artificial optical microscopy detection, an improved YOLOv5 algorithm was proposed to detect the density distribution of dislocation corrosion pits of monocrystalline silicon. The attention mechanism was introduced based on the original YOLOv5 algorithm to optimize the network structure and strengthen the calculation ability of the model. The network detection accuracy was further improved by strengthening the feature fusion, and the loss function was optimized to enhance the accuracy of positioning and improve the training speed. The experimental results show that the improved algorithm can detect monocrystalline-silicon dislocation pits of different corrosive fluids with accuracy of 93.52% and 98.82%, respectively, the mean average precision (mAP) can reach 96.17%, and the frame rate can reach 47 frame/s, which satisfies the requirements of real-time detection.