The detection of tiny defects in industrial products is important for improving the quality of industrial products and maintaining production safety. Currently, image-based defect detection methods are ineffective in detecting tiny and variously shaped defects. Therefore, this paper proposes a tiny defect detection network (TD-Net) for industrial products to improve the effectiveness of tiny defect detection. TD-Net improves the overall defect detection effect, especially the detection effect of tiny defects, by solving the problems of downsampling of tiny defects, pre-filtering of conflicting deep and shallow semantic information, and cascading fusion of multi-scale information. Specifically, this paper proposes the Defect Downsampling (DD) module to realize the defect information supplementation during the backbone downsampling process and improve the problem that the stepwise convolution easily misses the detection of tiny defects. Meanwhile, the Semantic Information Interaction Module (SIIM) is proposed, which fuses deep and shallow semantic features, and is designed to interact the fused features with shallow features to optimize the detection of tiny defects. Finally, the Scale Information Fusion Module (SIFM) is proposed to improve the Path Aggregation Network (PANet) for cascading fusion and information focus on different scale information, which enables further improvement of defect detection performance of TD-Net. Extensive experimental results on the NEU–DET data set (76.8 $$\%$$% mAP), the Peking University PCB defect data set (96.2 $$\%$$% mAP) and the GC10-DET data set (71.5 $$\%$$% mAP) show that the proposed TD-Net achieves competitive results compared with SOTA methods with the equivalent parameter quantity.

Aiming at the problems of low efficiency, high false detection rate, and poor real-time performance of current industrial defect detection methods, this paper proposes an industrial defect detection method based on an expanded perceptual field and feature fusion for practical industrial applications. First, to improve the real-time performance of the network, the original network structure is enhanced by using depth-separable convolution to reduce the computation while ensuring the detection accuracy, and the critical information extraction from the feature map is enhanced by using MECA (More Efficient Channel Attention) attention to the detection network. To reduce the loss of small target detail information caused by the pooling operation, the ASPF (Atrous Spatial Pyramid Fast) module is constructed using dilate convolution with different void rates to extract more contextual information. Secondly, a new feature fusion method is proposed to fuse more detailed information by introducing a shallower feature map and using a dense multiscale weighting method to improve detection accuracy. Finally, in the model optimization process, the K-means++ algorithm is used to reconstruct the prediction frame to speed up the model’s convergence and verify the effectiveness of the combination of the Mish activation function and the SIoU loss function. The NEU-DET steel dataset and PCB dataset is used to test the effectiveness of the proposed model, and compared to the original YOLOv5s, our method in terms of mAP metrics by 6.5% and 1.4%, and in F1 by 5.74% and 1.33%, enabling fast detection of industrial surface defects to meet the needs of real industry.

Effective log anomaly detection can help operators locate and solve problems quickly, ensure the rapid recovery of the system, and reduce economic losses. However, recent log anomaly detection studies have shown some drawbacks, such as concept drift, noise problems, and fuzzy feature relation extraction, which cause data instability and abnormal misjudgment, leading to significant performance degradation. This paper proposes a multi-feature deep fusion of an unstable log anomaly detection model (MDFULog) for the above problems. The MDFULog model uses a novel log resolution method to eliminate the dynamic interference caused by noise. This paper proposes a feature enhancement mechanism that fully uses the correlation between semantic information, time information, and sequence features to detect various types of log exceptions. The introduced semantic feature extraction model based on Bert preserves the semantics of log messages and maps them to log vectors, effectively eliminating worker randomness and noise injection caused by log template updates. An Informer anomaly detection classification model is proposed to extract practical information from a global perspective and predict outliers quickly and accurately. Experiments were conducted on HDFS, OpenStack, and unstable datasets, showing that the anomaly detection method in this paper performs significantly better than available algorithms.

In actual scenarios, industrial and cloud computing platforms usually need to monitor equipment and traffic anomalies through multivariable time series data. However, the existing anomaly detection methods can not capture the long-distance temporal correlations of data and the potential relationships between features simultaneously, and only have high detection accuracy for specific time sequence anomaly detection scenarios without good generalization ability. This paper proposes a time-series anomaly-detection framework for multiple scenarios, Anomaly-PTG (anomaly parallel transformer GRU), given the above limitations. The model uses the parallel transformer GRU as the information extraction module of the model to learn the long-distance correlation between timestamps and the global feature relationship of multivariate time series, which enhances the ability to extract hidden information from time series data. After extracting the information, the model learns the sequential representation of the data, conducts the sequential modeling, and transmits the data to the full connection layer for prediction. At the same time, it also uses the autoencoder to learn the potential representation of the data and reconstruct the data. The two are optimally combined to form an anomaly detection module of the model. The module combines timestamp prediction with time series data reconstruction, improving the detection rate of rare anomalies and detection accuracy. By using three public datasets of physical devices and one dataset of network traffic intrusion detection, the model’s effectiveness was verified, and the model’s generalization ability and strong robustness were demonstrated. Compared with the most advanced method, the average F1 value of the Anomaly-PTG model on four datasets was increased by 2.2%, and the F1 value on each dataset was over 94%.