Recently, many arbitrary-oriented object detection (AOOD) methods have been proposed and attracted widespread attention in many fields. However, most of them are based on anchor-boxes or standard Gaussian heatmaps. Such label assignment strategy may not only fail to reflect the shape and direction characteristics of arbitrary-oriented objects, but also have high parameter-tuning efforts. In this paper, a novel AOOD method called General Gaussian Heatmap Label Assignment (GGHL) is proposed. Specifically, an anchor-free object-adaptation label assignment (OLA) strategy is presented to define the positive candidates based on two-dimensional (2D) oriented Gaussian heatmaps, which reflect the shape and direction features of arbitrary-oriented objects. Based on OLA, an oriented-bounding-box (OBB) representation component (ORC) is developed to indicate OBBs and adjust the Gaussian center prior weights to fit the characteristics of different objects adaptively through neural network learning. Moreover, a joint-optimization loss (JOL) with area normalization and dynamic confidence weighting is designed to refine the misalign optimal results of different subtasks. Extensive experiments on public datasets demonstrate that the proposed GGHL improves the AOOD performance with low parameter-tuning and time costs. Furthermore, it is generally applicable to most AOOD methods to improve their performance including lightweight models on embedded platforms.

A few lightweight convolutional neural network (CNN) models have been recently designed for remote sensing object detection (RSOD). However, most of them simply replace vanilla convolutions with stacked separable convolutions (SConvs), which may not be efficient due to a lot of precision losses and may not be able to detect oriented bounding boxes (OBBs). Also, the existing OBB detection methods are difficult to constrain the shape of objects predicted by CNNs accurately. In this article, we propose an effective lightweight oriented object detector (LO-Det). Specifically, a channel separation-aggregation (CSA) structure is designed to simplify the complexity of SConvs, and a dynamic receptive field (DRF) mechanism is developed to maintain high accuracy by customizing the convolution kernel and its perception range dynamically when reducing the network complexity. The CSA-DRF component optimizes efficiency while maintaining high accuracy. Then, a diagonal support constraint head (DSC-Head) component is designed to detect OBBs and constrain their shapes more accurately and stably. Extensive experiments on public data sets demonstrate that the proposed LO-Det can run very fast even on embedded devices with the competitive accuracy of detecting oriented objects.

Object detection (OD) is an important task of computer vision and has been widely used in many fields, including remote sensing (RS). However, the complex scenes, large-scale variation, and dense instances of RS bring huge challenges to OD. To meet these challenges, a novel Nonlocal-aware Pyramid and Multiscale Multitask Refinement Detector (NPMMR-Det) is proposed. Specifically, nonlocal-aware pyramid attention (NP-Attention) is designed for guiding a neural network model to focus more on efficient features and suppress background noise. Then a multiscale refinement feature pyramid network (MSR-FPN) is proposed to fuse the multiscale context features extracted by the NP-Attention guided neural network and adjust the optimal receptive field. In order to use these features more effectively, a multitask refinement head called MTR-Head, with offset sharing and a modulation mechanism, is developed to refine the feature misalignment between the localization task and the classification task. Extensive experiments performed on two public RS data sets demonstrate that the proposed NPMMR-Det achieves competitive performance compared with state-of-the-art methods.

Accurate traffic prediction plays a pivotal role in the development of intelligent transportation systems. However, traffic data often exhibits multi-scale temporal dependencies, spatiotemporal heterogeneity, and highly nonlinear characteristics, which pose significant challenges to existing prediction models. To address these challenges, we propose a General Decoupled Graph Convolutional Recurrent Network (GDGCRN). At the core of GDGCRN is its ability to handle multi-scale temporal dynamics. By learning temporal embeddings for daily and weekly cycles, the model adaptively captures short-term fluctuations as well as long-term patterns. On the spatial side, GDGCRN incorporates a sensor-specific mechanism that adapts to the unique behavior of individual traffic sensors, allowing the model to capture complex spatial dependencies across diverse and heterogeneous traffic environments. Moreover, GDGCRN introduces a signal decoupling mechanism that separates steady-state and non-steady-state signals, enabling the model to robustly handle both predictable traffic trends and sudden disruptions. Through comprehensive experiments on multiple benchmark datasets, including traffic flow prediction on PeMS04 and PeMS08, demand prediction on the NYCBike dataset, and speed prediction on PeMSD7(M), we demonstrate that GDGCRN achieves highly competitive performance across these datasets. Notably, on the PeMS08 dataset, GDGCRN reduces the MAE metric by 9.92% compared to the MVSTT model.

Arbitrary-oriented object detection (AOOD) has been widely applied to locate and classify objects with diverse orientations in remote sensing images. However, the inconsistent features for the localization and classification tasks in AOOD models may lead to ambiguity and low-quality object predictions, which constrains the detection performance. In this article, an AOOD method called task-wise sampling convolutions (TS-Conv) is proposed. TS-Conv adaptively samples task-wise features from respective sensitive regions and maps these features together in alignment to guide a dynamic label assignment for better predictions. Specifically, sampling positions of the localization convolution in TS-Conv are supervised by the oriented bounding box (OBB) prediction associated with spatial coordinates, while sampling positions and convolutional kernel of the classification convolution are designed to be adaptively adjusted according to different orientations for improving the orientation robustness of features. Furthermore, a dynamic task-consistent-aware label assignment (DTLA) strategy is developed to select optimal candidate positions and assign labels dynamically according to ranked task-aware scores obtained from TS-Conv. Extensive experiments on several public datasets covering multiple scenes, multimodal images, and multiple categories of objects demonstrate the effectiveness, scalability, and superior performance of the proposed TS-Conv.

Recently, a lot of deep learning (DL) methods have been proposed for infrared small target detection (ISTD). A DL-based model for the ISTD task needs large amounts of samples. However, the diversity of existing ISTD datasets is not sufficient to train a DL model with good generalization. To solve this issue, a data augmentation method called Prior-Guided Data Augmentation (PGDA) is proposed to expand the diversity of training samples indirectly without additional training data. Specifically, it decouples the target description and localization abilities by preserving the scale distribution and physical characteristics of targets. Furthermore, a multi-scene infrared small target dataset (MSISTD) consisting of 1077 images with 1343 instances is constructed. The number of images and the number of instances in MSISTD are 2.4 times and 2.5 times than those of the existing largest real ISTD dataset Single-frame Infrared Small Target (SIRST) Benchmark, respectively. Extensive experiments on the SIRST dataset and the constructed MSISTD dataset illustrate that the proposed PGDA improves the performance of existing DL-based ISTD methods without extra model complexity burdens. In comparison with SIRST, MSISTD has been evaluated as a more comprehensive and accurate benchmark for ISTD tasks.