In automated industrial inspection,it is often necessary to train models on anomaly-free images and perform anomaly detection on products,which is also an important and challenging task in computer vision.The student-...In automated industrial inspection,it is often necessary to train models on anomaly-free images and perform anomaly detection on products,which is also an important and challenging task in computer vision.The student-teacher network trains students to regress the output of the teacher,and uses the difference between the output of the student network and the pre-trained teacher network to locate anomalies,which has achieved advanced results in the field of abnormal segmentation.However,it is slow to predict a picture,and no anomaly detection is performed.A multi-student teacher network is proposed,which uses multiple student networks to jointly regress the output of the teacher network,and the minimum square difference between the output of students and teachers in each dimension is selected as the difference value.The information in the middle layer of the network is used to represent each area of the image and calculate the anomaly distance for anomaly segmentation,and the maximum abnormal score is used to represent the abnormal degree of the image for abnormal detection.Experiments results on MVTec anomaly detection show that the algorithm predicts a picture in 0.17 s and can output anomaly detection results at the same time,with image AUROC reaching 91.1%and Pixel AUROC reaching 94.5%.On the wall tile dataset produced by taking pictures of real scenes,image AUROC reached 89.7%,and Pixel AUROC reached 89.1%.Compared with the original student-teacher network,the proposed method can quickly complete anomaly segmentation and anomaly detection tasks at the same time with better accuracy,and it also has better results in real applications.展开更多
Unlike existing fully-supervised approaches,we rethink colorectal polyp segmentation from an out-of-distribution perspective with a simple but effective self-supervised learning approach.We leverage the ability of mas...Unlike existing fully-supervised approaches,we rethink colorectal polyp segmentation from an out-of-distribution perspective with a simple but effective self-supervised learning approach.We leverage the ability of masked autoencoders-self-supervised vision transformers trained on a reconstruction task-to learn in-distribution representations,here,the distribution of healthy colon images.We then perform out-of-distribution reconstruction and inference,with feature space standardisation to align the latent distribution of the diverse abnormal samples with the statistics of the healthy samples.We generate per-pixel anomaly scores for each image by calculating the difference between the input and reconstructed images and use this signal for out-of-distribution(i.e.,polyp)segmentation.Experimental results on six benchmarks show that our model has excellent segmentation performance and generalises across datasets.Our code is publicly available at https://github.com/GewelsJI/Polyp-OOD.展开更多
基金National Natural Science Foundation of China(No.21706096)Natural Science Foundation of Jiangsu Province(No.BK20160162)。
文摘In automated industrial inspection,it is often necessary to train models on anomaly-free images and perform anomaly detection on products,which is also an important and challenging task in computer vision.The student-teacher network trains students to regress the output of the teacher,and uses the difference between the output of the student network and the pre-trained teacher network to locate anomalies,which has achieved advanced results in the field of abnormal segmentation.However,it is slow to predict a picture,and no anomaly detection is performed.A multi-student teacher network is proposed,which uses multiple student networks to jointly regress the output of the teacher network,and the minimum square difference between the output of students and teachers in each dimension is selected as the difference value.The information in the middle layer of the network is used to represent each area of the image and calculate the anomaly distance for anomaly segmentation,and the maximum abnormal score is used to represent the abnormal degree of the image for abnormal detection.Experiments results on MVTec anomaly detection show that the algorithm predicts a picture in 0.17 s and can output anomaly detection results at the same time,with image AUROC reaching 91.1%and Pixel AUROC reaching 94.5%.On the wall tile dataset produced by taking pictures of real scenes,image AUROC reached 89.7%,and Pixel AUROC reached 89.1%.Compared with the original student-teacher network,the proposed method can quickly complete anomaly segmentation and anomaly detection tasks at the same time with better accuracy,and it also has better results in real applications.
文摘Unlike existing fully-supervised approaches,we rethink colorectal polyp segmentation from an out-of-distribution perspective with a simple but effective self-supervised learning approach.We leverage the ability of masked autoencoders-self-supervised vision transformers trained on a reconstruction task-to learn in-distribution representations,here,the distribution of healthy colon images.We then perform out-of-distribution reconstruction and inference,with feature space standardisation to align the latent distribution of the diverse abnormal samples with the statistics of the healthy samples.We generate per-pixel anomaly scores for each image by calculating the difference between the input and reconstructed images and use this signal for out-of-distribution(i.e.,polyp)segmentation.Experimental results on six benchmarks show that our model has excellent segmentation performance and generalises across datasets.Our code is publicly available at https://github.com/GewelsJI/Polyp-OOD.
