1. 毕业设计（论文）的内容和要求
1.课题简介：基于机器视觉的工件表面缺陷检测是产品生产过程中对零件质量进行监测和控制的重要环节。
由于加工工艺、材质、成像参数等原因，工件表面表现出许多复杂光学成像的特征，如高反光、复杂纹理等，导致缺陷和非缺陷区域之间的低对比度，噪声和细微缺陷的相似性，缺陷的随机性，识别精度低等难题，使得缺陷检测工作变得困难。
而当前基于卷积神经网深度学习在语义分割和目标检测等计算机视觉领域获得巨大成功，也发展出了许多优秀模型。
2. 参考文献
[1]胡太,杨明, 结合目标检测的小目标语义分割算法. 南京大学学报(自然科学), 2019,55(1):73-84.[2] Redmon J , Farhadi A . YOLOv3: An Incremental Improvement. 2018.[3] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. InProceedings of the IEEE con-ference on computer vision and pattern recognition, 2016: 770778.[4] Long, Jonathan, Shelhamer, Evan, Darrell, Trevor. Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis Machine Intelligence, 2014, 39(4):640-651.[5] Olaf Ronneberger, Philipp Fischer, Thomas Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, LNCS, 2015. 9351: 234--241,[6] Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Advances in Neural Information Processing Systems 28.NIPS,2015.
