In response to the problems of difficult identification of degradation stage start points and inadequate extraction of degradation features in the current rolling bearing remaining life prediction method, a rolling bearing remaining life prediction method based on multi-scale feature extraction and attention mechanism is proposed. Firstly, this paper takes the normalized bearing vibration signal as input and adopts a quadratic function as the RUL prediction label, avoiding identifying the degradation stage start point. Secondly, the spatial and temporal features of the bearing vibration signal are extracted using the dilated convolutional neural network and LSTM network, respectively, and the channel attention mechanism is used to assign weights to each degradation feature to effectively use multi-scale information. Finally, the mapping of bearing degradation features to remaining life labels is achieved through a fully connected layer for the RUL prediction of bearings. The proposed method is validated using the PHM 2012 Challenge bearing dataset, and the experimental results show that the predictive performance of the proposed method is superior to that of other RUL prediction methods.

Category level 6D object pose estimation aims to predict the rotation, translation and size of object instances in any scene. In current research methods, global average pooling (first-order) is usually used to explore geometric features, which can only capture the first-order statistical information of the features and do not fully utilize the potential of the network. In this work, we propose a new high-order pose estimation network (HoPENet), which enhances feature representation by collecting high-order statistics to model high-order geometric features at each stage of the network. HoPENet introduces a global high-order enhancement module and utilizes global high-order pooling operations to capture the correlation between features and fuse global information. In addition, this module can capture long-term statistical correlations and make full use of contextual information. The entire network finally obtains a more discriminative feature representation. Experiments on two benchmarks, the virtual dataset CAMERA25 and the real dataset REAL275, demonstrate the effectiveness of HoPENet, achieving state-of-the-art (SOTA) pose estimation performance.

In missile docking, high-precision section alignment is vital for mission success. Traditional techniques, relying on radar and GPS, face calibration complexities and environmental interference risks, potentially leading to inaccurate estimations. To address this issue, the PoseNoCal-YOLOv5 is developed, offering a vision-based pose estimation approach that requires no traditional calibration, enhancing docking precision and reliability. It comprises two sub-networks: an improved YOLOv5 object detection model with an attention mechanism for precise object detection, and an uncalibrated pose estimation module using relinearization for pose estimation without camera calibration. A simulated dataset is created for validation, covering diverse docking scenarios. Extensive experiments on this simulated datasets prove the effectiveness of the proposed method.

Six-dimensional object pose estimation is a fundamental problem in the field of computer vision. Recently, category-level object pose estimation methods based on 3D-GC have made significant breakthroughs due to advancements in 3D-GC. However, current methods often fail to capture long-range dependencies, which are crucial for modeling complex and occluded object shapes. Additionally, discerning detailed differences between different objects is essential. Some existing methods utilize self-attention mechanisms or Transformer encoder–decoder structures to address the lack of long-range dependencies, but they only focus on first-order information of features, failing to explore more complex information and neglecting detailed differences between objects. In this paper, we propose SAPENet, which follows the 3D-GC architecture but replaces the 3D-GC in the encoder part with HS-layer to extract features and incorporates statistical attention to compute higher-order statistical information. Additionally, three sub-modules are designed for pose regression, point cloud reconstruction, and bounding box voting. The pose regression module also integrates statistical attention to leverage higher-order statistical information for modeling geometric relationships and aiding regression. Experiments demonstrate that our method achieves outstanding performance, attaining an mAP of 49.5 on the 5°2 cm metric, which is 3.4 higher than the baseline model. Our method achieves state-of-the-art (SOTA) performance on the REAL275 dataset.

Real-time object detection and pose estimation are critical in practical applications such as virtual reality, scene understanding, and robotics. In this paper, we propose a compact generalized non-local pose estimation network capable of directly predicting the projection of an object’s 3D bounding box vertices onto a 2D image, facilitating the estimation of the object’s 6D pose. The network is constructed using the YOLOv5 model, with the integration of an improved non-local module termed the Compact Generalized Non-local Block. This module enhances feature representation by learning the correlations between the positions of all elements across channels, effectively capturing subtle feature cues. The proposed network is end-to-end trainable, producing accurate pose predictions without the need for any post-processing operations. Extensive validation on the LineMod dataset shows that our approach achieves a final accuracy of 46.1% on the average 3D distance of model vertices (ADD) metric, outperforming existing methods by 6.9% and our baseline model by 1.8%, thus underscoring the efficacy of the proposed network.