计算机科学 ›› 2023, Vol. 50 ›› Issue (7): 137-142.doi: 10.11896/jsjkx.220500066
周波, 江佩峰, 段昶, 罗月童
ZHOU Bo, JIANG Peifeng, DUAN Chang, LUO Yuetong
摘要： 基于深度学习的目标检测算法在工业缺陷检测领域得到了充分推广与应用,但少有适用于工业检测场景中单一背景的算法。文中以工业检测场景中具有大量简单重复背景为出发点,对RetinaNet算法进行了如下改进:1)引入难负样本挖掘策略,减小了大量简单重复负样本对对模型拟合正样本的影响;2)设计了自适应忽略样本选择策略,避免与正样本交并比高的样本混入负样本而混淆模型训练;3)简化了RetinaNet的分类子网络,降低了模型改进后的过拟合风险。在公开的PCB缺失孔数据集及自建的LED气泡数据集上,相比RetinaNet算法,改进后的方法在召回率上分别提升了14.1%和1.8%,在精确率上分别提升了3.6%和0.4%,表明改进后的方法能显著提升单一背景下的目标检测水平。
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| [1]REN S,HE K,GIRSHICK R,et al.Faster r-cnn:Towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems.2015:91-99. [2]DAI J,LI Y,HE K,et al.R-fcn:Object detection via region-based fully convolutional networks[J].Advances in Neural Information Processing Systems,2016,29:379-387. [3]BOCHKOVSKIY A,WANG C Y,LIAO H Y M.Yolov4:Optimal speed and accuracy of object detection[J].arXiv:2004.10934,2020. [4]ZHU X,LYU S,WANG X,et al.TPH-YOLOv5:ImprovedYOLOv5 based on transformer prediction head for object detection on drone-captured scenarios[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:2778-2788. [5]GE Z,LIU S,WANG F,et al.Yolox:Exceeding yolo series in 2021[J].arXiv:2107.08430,2021. [6]LIU W,ANGUELOV D,ERHAN D,et al.Ssd:Single shotmultibox detector[C]//European Conference on Computer Vision.Cham:Springer,2016:21-37. [7]LIN T Y,GOYAL P,GIRSHICK R,et al.Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:2980-2988. [8]LAW H,DENG J.Cornernet:Detecting objects as paired key-points[C]//Proceedings of the European Conference on Computer Vision(ECCV).2018:734-750. [9]DUAN K,BAI S,XIE L,et al.Centernet:Keypoint triplets for object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:6569-6578. [10]TIAN Z,SHEN C,CHEN H,et al.Fcos:Fully convolutional one-stage object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:9627-9636. [11]LIU Z,LIN Y,CAO Y,et al.Swin transformer:Hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:10012-10022. [12]ZHANG S,WEN L,BIAN X,et al.Single-shot refinement neural network for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:4203-4212. [13]KONG T,SUN F,YAO A,et al.Ron:Reverse connection with objectness prior networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:5936-5944. [14]ZHANG S,CHI C,YAO Y,et al.Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection[J].arXiv:1912.02424,2019. [15]HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778. [16]LIN T Y,DOLLÁR P,GIRSHICK R,et al.Feature pyramidnetworks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:2117-2125. [17]LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:3431-3440. [18]ZHANG S,BENENSON R,SCHIELE B.Citypersons:A diverse dataset for pedestrian detection[C]//Proceedings of the IEEE Conference on Computer Vision Andpattern Recognition.2017:3213-3221. [19]ZHUGE Y.The Quest for Machine Learning[M].Beijing:Posts and Telecommunications Press,2018. |
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