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Over the past few years, target detectors that utilize Convolutional Neural Networks have gained extensive application in the domain of remote sensing (RS) imagery. Recently, optimizing bounding boxes has consistently been a hot topic in the research field. However, existing methods often fail to take into account the interference caused by the shape and orientation changes of RS targets with high aspect ratios during training, leading to challenges in boundary perception when dealing with RS targets that have large aspect ratios. To deal with this challenge, our study introduces the Adaptive Boundary Perception Network (ABP-Net), a novel two-stage approach consisting of pre-training and training phases, which enhances the boundary perception of CNN-based detectors. In the pre-training phase, involving the initialization of our model’s backbone network and the label assignment, the traditional label assignment with a fixed IoU threshold fails to fully cover the critical information of slender targets, resulting in the detector missing lots of high-quality positive samples. To overcome this drawback, we design a Shape-Sensitive (S-S) label assignment strategy that can improve the boundary shape perception by dynamically adjusting the IoU threshold according to the aspect ratios of the targets so that the high-quality samples with critical features can be divided into positive samples. Moreover, during the training phase, minor angle differences of the slender bounding box may cause a significant change in the value of the loss function, producing unstable gradients. Such drastic gradient changes make it difficult for the model to find a stable update direction when optimizing the bounding box parameters, resulting in difficulty with the model convergence. To this end, we propose the Robust–Refined loss function (R-R), which can enhance the boundary localization perception by focusing on low-error samples and suppressing the gradient amplification of difficult samples, thereby improving the model stability and convergence. Experiments on UCAS-AOD and HRSC2016 datasets validate our specialized detector for high-aspect-ratio targets, improving performance, efficiency, and accuracy with straightforward operation and quick deployment.

Current remote sensing (RS) detectors often rely on predefined anchor boxes with fixed angles to handle the multi-directional variations of targets. This approach makes it challenging to accurately select regions of interest and extract features that align with the direction of the targets. Most existing regression methods also adopt angle regression to match the attributes of remote sensing detectors. Due to the inconsistent regression direction and massive anchor boxes with a high aspect ratio, the extracted target features change greatly, the loss function changes drastically, and the training is unstable. However, existing RS detectors and regression techniques have not been able to effectively balance the precision of directional feature extraction with the complexity of the models. To address these challenges, this paper introduces a novel approach known as Dynamic Direction Learning R-CNN (DDL R-CNN), which comprises a dynamic direction learning (DDL) module and a boundary center region offset generation network (BC-ROPN). The DDL module pre-extracts the directional features of targets to provide a coarse estimation of their angles and the corresponding weights. This information is used to generate rotationally aligned anchor boxes that better model the directional features of the targets. BC-ROPN represents an innovative method for anchor box regression. It utilizes the central features of the maximum bounding rectangle’s width and height, along with the coarse angle estimation and weights derived from DDL module, to refine the orientation of the anchor box. Our method has been proven to surpass existing rotating detection networks in extensive testing across two widely used remote sensing detection datasets, namely UCAS-AOD and HRSC2016.

The characteristics of multi-directional rotation and high aspect ratio of targets such as shipwrecks lead to low detection accuracy and difficulty localizing existing detection models for this target type. Through our research, we design three main inconsistencies in rotating target detection compared to traditional target detection, i.e., inconsistency between target and anchor frame, inconsistency between classification features and regression features, and inconsistency between rotating frame quality and label assignment strategy. In this paper, to address the discrepancies in the above three aspects, we propose the Side-scan Sonar Dynamic Rotating Target Detector (S3DR-Det), which is a model with a dynamic rotational convolution (DRC) module designed to effectively gather rotating targets’ high-quality features during the model’s feature extraction phase, a feature decoupling module (FDM) designed to distinguish between the various features needed for regression and classification in the detection phase, and a dynamic label assignment strategy based on spatial matching prior information (S-A) specific to rotating targets in the training phase, which can more reasonably and accurately classify positive and negative samples. The three modules not only solve the problems unique to each stage but are also highly coupled to solve the difficulties of target detection caused by the multi-direction and high aspect ratio of the target in the side-scan sonar image. Our model achieves an average accuracy (AP) of 89.68% on the SSUTD dataset and 90.19% on the DNASI dataset. These results indicate that our model has excellent detection performance.

The field of remote sensing target detection has experienced rapid development in recent years, demonstrating significant value in various applications. However, general detection algorithms still face many key challenges when dealing with directional target detection: firstly, conventional networks struggle to accurately represent features of rotated targets, particularly in modeling the slender shape characteristics of high-aspect-ratio targets; secondly, the mismatch between the static label allocation strategy and the feature space of dynamic rotating targets leads to bias in training sample selection under extreme-aspect-ratio scenarios. To address these issues, this paper proposes a single-stage Shape-Aware Dynamic Alignment Network (SADA-Net) that collaboratively enhances detection accuracy through feature representation optimization and adaptive label matching. The network’s design philosophy demonstrates greater flexibility and complementarity than that of previous models. Specifically, a Dynamic Refined Rotated Convolution Module (DRRCM) is designed to achieve rotation-adaptive feature alignment. An Anchor-Refined Feature Alignment Module (ARFAM) is further constructed to correct feature-to-spatial misalignment. In addition, a Shape-Aware Quality Assessment (SAQA) strategy is proposed to optimize sample matching quality based on target shape information. Experiment results demonstrate that SADA-Net achieves excellent performance comparable to state-of-the-art methods on three widely used remote sensing datasets (i.e., HRSC2016, DOTA, and UCAS-AOD).

Current mainstream remote sensing target detection algorithms mostly estimate the rotation angle of targets by designing different bounding box descriptions and loss functions. However, they fail to consider the symmetry–asymmetry duality anisotropy in the distribution of key features required for target localization. Moreover, the equivalent feature extraction mode of shared convolutional kernels may lead to difficulties in accurately predicting parameters with different attributes, thereby reducing the performance of the detector. In this paper, we propose the Feature Equalization and Hierarchical Decoupling Network (FEHD-Net), which comprises three core components: a Symmetry-Enhanced Parallel Interleaved Convolution Module (PICM), a Parameter Decoupling Module (PDM), and a Critical Feature Matching Loss Function (CFM-Loss). PICM captures diverse spatial features over long distances by integrating square convolution and multi-branch continuous orthogonal large kernel strip convolution sequences, thereby enhancing the network’s capability in processing long-distance spatial information. PDM decomposes feature maps with different properties and assigns them to different regression branches to estimate the parameters of the target’s rotating bounding box. Finally, to stabilize the training of anchors with different qualities that have captured the key features required for detection, CFM-Loss utilizes the intersection ratio between anchors and true value labels, as well as the uncertainty of convolutional regression during training, and designs an alignment criterion (symmetry-aware alignment) to evaluate the regression ability of different anchors. This enables the network to fine-tune the processing of templates with different qualities, achieving stable training of the network. A large number of experiments demonstrate that compared with existing methods, FEHD-Net can achieve state-of-the-art performance on DOTA, HRSC2016, and UCAS-AOD datasets.

We present a study of laser-ion acceleration, where an increased laser spot size leads to sheath field geometries that accelerate ion beams of narrow and achromatic divergence at unprecedented charge densities, resulting from the high aspect ratio of the laser spot size to the acceleration distance. Matching the laser pulse length to the transverse laser spot size mitigated laser sweeping across the target front side and optimized the acceleration, with maximum energies deviating significantly from a linear intensity scaling.

Object detection in aerial images has had a broader range of applications in the past few years. Unlike the targets in the images of horizontal shooting, targets in aerial photos generally have arbitrary orientation, multi-scale, and a high aspect ratio. Existing methods often employ a classification backbone network to extract translation-equivariant features (TEFs) and utilize many predefined anchors to handle objects with diverse appearance variations. However, they encounter misalignment at three levels, spatial, feature, and task, during different detection stages. In this study, we propose a model called the Staged Adaptive Alignment Detector (SAADet) to solve these challenges. This method utilizes a Spatial Selection Adaptive Network (SSANet) to achieve spatial alignment of the convolution receptive field to the scale of the object by using a convolution sequence with an increasing dilation rate to capture the spatial context information of different ranges and evaluating this information through model dynamic weighting. After correcting the preset horizontal anchor to an oriented anchor, feature alignment is achieved through the alignment convolution guided by oriented anchor to align the backbone features with the object’s orientation. The decoupling of features using the Active Rotating Filter is performed to mitigate inconsistencies due to the sharing of backbone features in regression and classification tasks to accomplish task alignment. The experimental results show that SAADet achieves equilibrium in speed and accuracy on two aerial image datasets, HRSC2016 and UCAS-AOD.

Cranes are pivotal heavy equipment used in the construction of transmission line scenarios. Accurately identifying these cranes and monitoring their status is pressing. The rapid development of computer vision brings new ideas to solve these challenges. Since cranes have a high aspect ratio, conventional horizontal bounding boxes contain a large number of redundant objects, which deteriorates the accuracy of object detection. In this study, we use a rotating target detection paradigm to detect cranes. We propose the YOLOv8-Crane model, where YOLOv8 serves as a detection network for rotating targets, and we incorporate Transformers in the backbone to improve global context modeling. The Kullback–Leibler divergence (KLD) with excellent scale invariance is used as a loss function to measure the distance between predicted and true distribution. Finally, we validate the superiority of YOLOv8-Crane on 1405 real-scene data collected by ourselves. Our approach demonstrates a significant improvement in crane detection and offers a new solution for enhancing safety monitoring.

All countries in Sub-Saharan Africa (SSA) face the prospect of a substantial increase in the number of primary school completers in the coming years. Although initial conditions vary widely from country to country, this increase will inevitably intensify pressure on the education system, particularly at the secondary and tertiary levels. African countries may thus find it timely to align their education policies and strategies to the emerging challenges. A key goal is to ensure that the education system continues to develop in an efficient, equitable, and fiscally sustainable manner even as it expands to accommodate the rising numbers seeking a place in secondary and tertiary education. The rest of this report is organized as follows. Chapter two elaborates the policy context for education development in SSA. Chapter three explains the methodology and data sources. Chapter four examines the challenges and constraints posed by the sheer volume of increases in enrollments in post-primary education with which most education systems in SSA must grapple in the coming years. Taking these constraints into account, the report evaluates the scope for policy development from three perspectives in the subsequent chapters: the coverage of education systems (chapter five), the quality and cost of service delivery (chapter six), and the division of financing by public and private sources (chapter seven). The fiscal implications of plausible policy packages that SSA countries might consider are assessed in chapter eight. Chapter nine seems up the general conclusions of the report.