In view of the existence of remote sensing images with large variations in spatial resolution, small and dense objects, and the inability to determine the direction of motion, all these components make object detection from remote sensing images very challenging. In this paper, we propose a single-stage detection network based on YOLOv5. This method introduces the MS Transformer module at the end of the feature extraction network of the original network to enhance the feature extraction capability of the network model and integrates the Convolutional Block Attention Model (CBAM) to find the attention area in dense scenes. In addition, the YOLOv5 target detection network is improved by incorporating a rotation angle approach from the a priori frame design and the bounding box regression formulation to make it suitable for rotating frame-based detection scenarios. Finally, the weighted combination of the two difficult sample mining methods is used to improve the focal loss function, so as to improve the detection accuracy. The average accuracy of the test results of the improved algorithm on the DOTA data set is 77.01%, which is higher than the previous detection algorithm. Compared with the average detection accuracy of YOLOv5, the average detection accuracy is improved by 8.83%. The experimental results show that the algorithm has higher detection accuracy than other algorithms in remote sensing scenes.

Lithium-ion batteries are widely used in many fields, and accurate prediction of their remaining useful life (RUL) was crucial for effective battery management and safety assurance. In order to solve the problem of reduced RUL prediction accuracy caused by the local capacity regeneration phenomenon during battery capacity degradation, this paper proposed a novel RUL prediction method, which combined complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique with an innovative hybrid prediction strategy that integrated the support vector regression (SVR) and the long short-term memory (LSTM) networks. First, CEEMDAN was used to decompose the battery capacity data into high-frequency and low-frequency components, thereby reducing the impact of capacity regeneration. Subsequently, the SVR model predicted the low-frequency component that characterized the main degradation trend, and the high-frequency component that contained capacity regeneration features was predicted using an LSTM network optimized by the sparrow search algorithm (SSA). Finally, the final RUL prediction was obtained by combining the predictions of the two models. Experimental results on NASA public datasets showed that the proposed hybrid method significantly outperformed existing methods: the RMSE of the methods proposed in this paper were all less than 0.0086 Ah, the MAE were all less than 0.0060 Ah, the R² values were all higher than 0.96, and the RUL prediction errors were controlled within one cycle. This method gave full play to the complementary advantages of SVR and LSTM and provided an accurate and reliable solution for RUL prediction of lithium-ion batteries.

Cracks are a common type of road distress. However, the traditional manual and vehicle-borne methods of detecting road cracks are inefficient, with a high rate of missed inspections. The development of unmanned aerial vehicles (UAVs) and deep learning has led to their use in crack detection and classification becoming an increasingly popular topic. In this paper, an aerial drone is used to efficiently and safely collect road data. However, this also brings many challenges. For example, flying too high or too fast may produce poor quality images, with unclear cracks that may be ignored or misjudged as other features and increased environmental noise that may make it difficult to distinguish between cracks and other noise features. To address the above challenges, this paper proposes the CrackNet model and CrackClassification algorithm. The CrackNet network is an encoder–decoder architecture. Low- and high-level semantic information are combined through the skip feature fusion layers between the encoder and decoder to enhance the model’s expression and ability to recover image details. Additionally, the MHDC module at the bottom of the network can significantly increase the receptive field without reducing the feature map resolution. The MHSA module can simultaneously capture features from multiple subspaces. The average precision (AP) scores of the CrackNet network on three datasets, namely UAVRoadCrack, CRKWH100, and CrackLS315, were 0.665, 0.942, and 0.895, respectively. In addition, values of the other two evaluation metrics, ODS and OIS, were the highest among the compared methods. Meanwhile, the proposed CrackClassification algorithm in this paper achieves 85% classification accuracy for transverse and longitudinal cracks and 78% classification accuracy for block cracks and reticulated cracks. Overall, the CrackNet algorithm provides a new baseline model for crack detection in UAV remote sensing image scenes. The CrackClassification algorithm provides a new approach for batch classification of highway cracks. The detection and classification algorithm proposed in this paper were applied to 108 km of road sections.

Infrastructure along the highway refers to various facilities and equipment: bridges, culverts, traffic signs, guardrails, etc. New technologies such as artificial intelligence, big data, and the Internet of Things are driving the digital transformation of highway infrastructure towards the future goal of intelligent roads. Drones have emerged as a promising application area of intelligent technology in this field. They can help achieve fast and precise detection, classification, and localization of infrastructure along highways, which can significantly enhance efficiency and ease the burden on road management staff. As the infrastructure along the road is exposed to the outdoors for a long time, it is easily damaged and obscured by objects such as sand and rocks; on the other hand, based on the high resolution of the images taken by Unmanned Aerial Vehicles (UAVs), the variable shooting angles, complex backgrounds, and high percentage of small targets mean the direct use of existing target detection models cannot meet the requirements of practical applications in industry. In addition, there is a lack of large and comprehensive image datasets of infrastructure along highways from UAVs. Based on this, a multi-classification infrastructure detection model combining multi-scale feature fusion and an attention mechanism is proposed. In this paper, the backbone network of the CenterNet model is replaced with ResNet50, and the improved feature fusion part enables the model to generate fine-grained features to improve the detection of small targets; furthermore, the attention mechanism is added to make the network focus more on valuable regions with higher attention weights. As there is no publicly available dataset of infrastructure along highways captured by UAVs, we filter and manually annotate the laboratory-captured highway dataset to generate a highway infrastructure dataset. The experimental results show that the model has a mean Average Precision (mAP) of 86.7%, an improvement of 3.1 percentage points over the baseline model, and the new model performs significantly better than other detection models overall.

The self-attention mechanism has an excellent ability to capture long-range dependencies of data. To enable the self-attention mechanism in point cloud tasks to focus on both local and global contexts, we design a separable self-attention mechanism for point clouds by decomposing the construction of the attention map of a point cloud into two steps: Intra-patch Attention and Inter-patch Attention, the former computes the attention map of the tokens corresponding to each point in the local patch of the point cloud for mining local fine-grained semantic relationships, while the latter constructs the attention map among all the patches for mining long-distance interaction information. The two self-attention mechanisms work in parallel, focusing on both fine-grained local patterns and considering global scenes. Equipped with Intra-patch Attention and Inter-patch Attention modules, we construct a hierarchical end-to-end point cloud analysis architecture called Separable Transformer and conduct exhaustive experiments to demonstrate that the performance of the network proposed in this paper is highly competitive with state-of-the-art methods.

Point cloud processing based on deep learning is developing rapidly. However, previous networks failed to simultaneously extract inter-feature interaction and geometric information. In this paper, we propose a novel point cloud analysis module, CGR-block, which mainly uses two units to learn point cloud features: correlated feature extractor and geometric feature fusion. CGR-block provides an efficient method for extracting geometric pattern tokens and deep information interaction of point features on disordered 3D point clouds. In addition, we also introduce a residual mapping branch inside each CGR-block module for the further improvement of the network performance. We construct our classification and segmentation network with CGR-block as the basic module to extract features hierarchically from the original point cloud. The overall accuracy of our network on the ModelNet40 and ScanObjectNN benchmarks achieves 94.1% and 83.5%, respectively, and the instance mIoU on the ShapeNet-Part benchmark also achieves 85.5%, proving the superiority of our method.