With the rapid improvement of Industrial Internet of Things and artificial intelligence, predictive maintenance (PdM) has attracted great attention from both academia and industrial practitioners. When equipment is running, the electrical attributes have intrinsic relations. Meanwhile, they are changing over time. However, existing PdM models are often limited as they lack considering both attribute interactions and temporal dependence of the dynamic working system. To address the problem, in this article, we propose an electrical spatio-temporal graph convolutional network (Electrical-STGCN) for PdM. First, it takes a sequence of electrical records as input. Next, both attribute interactions and temporal dependence are established to extract features. Then, the extracted features are fed into a prediction component. Finally, the output of the Electrical-STGCN (i.e., remaining useful life) can help the workers decide whether to carry out equipment maintenance. The effectiveness of the proposed method is verified in real-world cases. Our method achieves 85.2% Accuracy and 0.9 F1-Score, which are better than the other approaches.

Objects with different orientations are ubiquitous in the real world ( e.g. , texts/hands in the scene image, objects in the aerial image, etc. ), and the widely-used axis-aligned bounding box does not compactly enclose the oriented objects. Thus arbitrarily-oriented object detection has attracted rising attention in recent years. In this paper, we propose a novel and effective model to detect arbitrarily-oriented objects. Instead of directly predicting the angles of oriented bounding boxes like most existing methods, we evolve the axis-aligned bounding box to the oriented quadrilateral box with the assistance of dynamically gathering contour information. More specifically, we first obtain the axis-aligned bounding box in an anchor-free manner. After that, we set the key points based on the sampled contour points of the axis-aligned bounding box. To improve the localization performance, we enrich the feature representations of these key points by exploiting a dynamic information gathering mechanism. This technique propagates the geometrical and semantic information along the sampled contour points, and fuses the information from the semantic neighbors of each sampled point, which varies for different locations. Finally, we estimate the offsets between the axis-aligned bounding box key points and the oriented quadrilateral box corner points. Extensive experiments on two frequently-used aerial image benchmarks HRSC2016 and DOTA, as well as scene text/hand datasets ICDAR2015, TD500, and Oxford-Hand, demonstrate the effectiveness and advantage of our proposed model.

Scene text detection has attracted increasing concerns with the rapid development of deep neural networks in recent years. However, existing scene text detectors may overfit on the public datasets due to the limited training data, or generate inaccurate localization for arbitrary-shape scene texts. This paper presents an arbitrary-shape scene text detection method that can achieve better generalization ability and more accurate localization. We first propose a Scale-Aware Data Augmentation (SADA) technique to increase the diversity of training samples. SADA considers the scale variations and local visual variations of scene texts, which can effectively relieve the dilemma of limited training data. At the same time, SADA can enrich the training minibatch, which contributes to accelerating the training process. Furthermore, a Shape Similarity Constraint (SSC) technique is exploited to model the global shape structure of arbitrary-shape scene texts and backgrounds from the perspective of the loss function. SSC encourages the segmentation of text or non-text in the candidate boxes to be similar to the corresponding ground truth, which is helpful to localize more accurate boundaries for arbitrary-shape scene texts. Extensive experiments have demonstrated the effectiveness of the proposed techniques, and state-of-the-art performances are achieved over public arbitrary-shape scene text benchmarks (e.g., CTW1500 , Total-Text and ArT ).

Face recognition technology is increasingly being integrated into our daily life, e.g. Face ID. With the advancement of machine learning algorithms, the personal information such as age, gender, and race can be easily deduced from the recorded face images in these applications. This poses a serious privacy threat to individuals who do not want to be profiled, as face images are collected for biometric purposes. Existing methods mostly focus on adding the invisible adversarial perturbations into the images to make automatic inference infeasible. However, the application scenarios of these methods are limited due to the perturbations depending on the specific model. In this paper, we introduce a novel face privacy-enhancing framework by obfuscating the stored faces, which could maintain the data utility (face identity) while protecting the privacy of users (facial attributes). Specifically, we first develop a feature attribution module to discover the identity-related facial parts. Within this module, we introduce a pixel importance estimation model based on Shapley value to obtain a pixel-level attribution map, and then each pixel on the attribution map is aggregated into semantic facial parts, which are used to quantify the importance of different facial parts. Next, we design a privacy-enhancing module to generate the high-quality obfuscated images, which can modify the privacy semantic content and preserve the identity-related information. Using the proposed method, users can choose the single or multiple attributes to be obfuscated without affecting identity matching. Extensive experiments conducted on CelebA-HQ and VGGFace2-HQ benchmarks demonstrate the effectiveness and generalization ability of our method.

Scene text plays a significant role in image and video understanding, which has made great progress in recent years. Most existing models on text detection in the wild have the assumption that all the texts are surrounded by a rotated rectangle or quadrangle. While there also exist lots of curved texts in the wild, which would not be bounded by a regular bounding box. In this paper, we develop a novel architecture to localize the text regions, which can deal with curved-shape scene texts. Specifically, we first design a text-related feature enhancement module by incorporating the prior knowledge of the text shape to enhance the feature representations. After that, based on the enhanced features, we employ a region proposal network to generate the candidate boxes of scene texts. For each text candidate, a pyramid region-of-interest pooling attention module is utilized to extract the fixed-size features. Finally, we exploit the box-aware context-based text segmentation module and box refinement network to obtain the location of scene text. Experiments are conducted on four challenging benchmarks CTW1500, totalTEXT, ICDAR-2015 and MLT, and the experimental results have demonstrated the superiority of our model.

Acquiring large amounts of high-quality real sonar data for object detection of autonomous underwater vehicles (AUVs) is challenging. Synthetic data can be an alternative, but it is hard to generate diverse data using traditional generative models when real data are limited. This study proposes a novel style transfer method, i.e., the multigranular feature alignment cycle-consistent generative adversarial network (CycleGAN), to generate sonar images leveraging remote sensing images, which can alleviate the dependence on real sonar data. Specifically, we add a spatial attention-based feature aggregation module to preserve unique features by attending to instance parts of an image. A pair of cross-domain discriminators are designed to guide generators to produce images that capture sonar styles. We also introduce a novel cycle consistency loss based on the discrete cosine transform of images, which better utilizes features that are evident in the frequency domain. Extensive experimental results show that the generated sonar images have better quality than CycleGAN, with improvements of 15.2% in IS, 56.9% in FID, 42.6% in KID, and 7.6% in learned perceptual image patch similarity, respectively. Moreover, after expanding the real sonar dataset with generated data, the average accuracy of the object detector, e.g., YOLOv6, has increased by more than 48.7%, indicating the effectiveness of the generated sonar data by our method.