深度学习陶瓷表面缺陷检测算法研究

传统的陶瓷缺陷检测主要依赖人工目测或放大镜观察，为解决检测效率低、结果主观性强等问题，提出了一种基于深度学习的陶瓷表面缺陷检测算法，针对于陶瓷杯表面的缺陷具体情况，在YOLOv5目标检测模型的基础上，增加小目标检测层，同时使用位置注意力机制进行特征重构提高检测的精确度，实现了高精度的缺陷检测。针对实际生产中的陶瓷双层杯进行数据采集训练，并对于每批数据进行推理，最终平均检测精度达到了95.4%。本文所改进的YOLOv5缺陷检测模型拥有更高的准确率、识别速度快等优点，可以极大地减少陶瓷质检减少人力物力的损耗与时间成本。

Deep learning ceramic surface defect detection algorithm research

Traditional ceramic defect detection mainly relies on manual visual inspection or magnifying glass observation. In order to solve the problems of low detection efficiency and strong subjectivity of results, this paper proposes a ceramic surface defect detection algorithm based on deep learning. According to the specific situation of the defects on the surface of the ceramic cup, a small target detection layer is added on the basis of the YOLOv5 target detection model, at the same time, the position attention mechanism is used for feature reconstruction to improve the detection accuracy, and high-precision defect detection is achieved. According to the actual production of ceramic double-layer cup data acquisition training, and reasoning for each batch of data, the final average detection accuracy reached 95.4%. The improved YOLOv5 defect detection model in this paper has the advantages of higher accuracy and faster recognition speed, which can greatly reduce the loss of human and material resources and time cost in ceramic quality inspection.