缺陷检测在工业生产过程中是不可或缺的一步，目前人工检测存在效率低及成本高的问题，提出了一种基于深度学习的陶瓷小目标缺陷检测算法。针对小目标缺陷，本文算法首先添加切片预训练层，降低大尺寸图像对显卡内存资源的损耗；其次为小目标缺陷的检测添加小目标检测层，并去除大目标检测层，以减少参数量；另外提出一种基于MLCA（mixed local channel attention）的特征选择融合模块，提高对小目标缺陷的感知能力；最后设计了一种共享参数的检测头，进一步降低算法的可学习参数数量。通过与基线模型对比，以陶瓷杯为例，本文算法的检测精度提升了20.9%，结合研制的检测软件及实验平台，陶瓷杯检测效率提升了46.9%。

Defect detection is regarded as an indispensable step in the industrial production process. At present, manual detection is faced with the problems of low efficiency and high cost. A ceramic small target defect detection algorithm based on deep learning was proposed. For small target defects, a slice pre-training layer was first added to reduce the loss of graphics memory resources by large-size images. Secondly, a small target detection layer was added for the detection of small target defects, and a large target detection layer was removed to reduce the number of parameters. In addition, a feature selection fusion module based on MLCA (mixed local channel attention) was proposed to improve the perception of small target defects. Finally, a detection head with shared parameters was designed to further reduce the number of learnable parameters of the algorithm. By comparing with the baseline model, taking the ceramic cup as an example, the detection accuracy of this algorithm has been improved by 20.9%. Combined with the developed detection software and experimental platform, the detection efficiency of the ceramic cup has been enhanced by about 46.9%.