Explore the vast range of titles available.

A multi-scale UAV aerial image object detection model MS-YOLOv7 based on YOLOv7 was proposed to address the issues of a large number of objects and a high proportion of small objects that commonly exist in the Unmanned Aerial Vehicle (UAV) aerial image. The new network is developed with a multiple detection head and a CBAM convolutional attention module to extract features at different scales. To solve the problem of high-density object detection, a YOLOv7 network architecture combined with the Swin Transformer units is proposed, and a new pyramidal pooling module, SPPFS is incorporated into the network. Finally, we incorporate the SoftNMS and the Mish activation function to improve the network’s ability to identify overlapping and occlusion objects. Various experiments on the open-source dataset VisDrone2019 reveal that our new model brings a significant performance boost compared to other state-of-the-art (SOTA) models. Compared with the YOLOv7 object detection algorithm of the baseline network, the mAP0.5 of MS-YOLOv7 increased by 6.0%, the mAP0.95 increased by 4.9%. Ablation experiments show that the designed modules can improve detection accuracy and visually display the detection effect in different scenarios. This experiment demonstrates the applicability of the MS-YOLOv7 for UAV aerial photograph object detection.

Although existing anchor-based oriented object detection methods have achieved remarkable results, they require manual preset boxes, which introduce additional hyper-parameters and calculations. These methods often use more complex architectures for better performance, which makes them difficult to deploy on computationally constrained embedded platforms, such as satellites and unmanned aerial vehicles. We aim to design a high-performance algorithm that is simple, fast, and easy to deploy for aerial image detection. In this article, we propose a one-stage anchor-free rotated object detector, FCOSR, that can be deployed on most platforms and uses our well-defined label assignment strategy for the features of the aerial image objects. We use the ellipse center sampling method to define a suitable sampling region for an oriented bounding box (OBB). The fuzzy sample assignment strategy provides reasonable labels for overlapping objects. To solve the problem of insufficient sampling, we designed a multi-level sampling module. These strategies allocate more appropriate labels to training samples. Our algorithm achieves an mean average precision (mAP) of 79.25, 75.41, and 90.13 on the DOTA-v1.0, DOTA-v1.5, and HRSC2016 datasets, respectively. FCOSR demonstrates a performance superior to that of other methods in single-scale evaluation, where the small model achieves an mAP of 74.05 at a speed of 23.7 FPS on an RTX 2080-Ti GPU. When we convert the lightweight FCOSR model to the TensorRT format, it achieves an mAP of 73.93 on DOTA-v1.0 at a speed of 17.76 FPS on a Jetson AGX Xavier device with a single scale.

Nowadays, wireless sensor networks (WSNs) have a significant and long-lasting impact on numerous fields that affect all facets of our lives, including governmental, civil, and military applications. WSNs contain sensor nodes linked together via wireless communication links that need to relay data instantly or subsequently. In this paper, we focus on unmanned aerial vehicle (UAV)-aided data collection in wireless sensor networks (WSNs), where multiple UAVs collect data from a group of sensors. The UAVs may face some static or moving obstacles (e.g., buildings, trees, static or moving vehicles) in their traveling path while collecting the data. In the proposed system, the UAV starts and ends the data collection tour at the base station, and, while collecting data, it captures images and videos using the UAV aerial camera. After processing the captured aerial images and videos, UAVs are trained using a YOLOv8-based model to detect obstacles in their traveling path. The detection results show that the proposed YOLOv8 model performs better than other baseline algorithms in different scenarios—the F1 score of YOLOv8 is 96% in 200 epochs.

In the process of UAV small target vehicle detection, it is difficult to extract the features because of the small target shape of the vehicle, the environment noise is big, the vehicles are dense and easy to miss detection. The LMAD-YOLO model is proposed, and the MultiEdgeEnhancer module is designed to enhance the edge information and enhance the feature capture through a series of operations. Large Separable Kernel Attention and SPPF are combined to form MSPF module, which can realize multi-scale perception aggregation and improve the ability of distinguishing small targets from interference. Adown module is introduced to replace the model of sampling, in order to reduce the parameters and computational complexity while enhancing the accuracy of small target detection. A Multidimensional Diffusion Fusion Pyramid Network is designed, in which Dasi and feature spread mechanism are used to fuse features to reduce the error detection and missed detection. Compared with YOLO11n model P, R, MAP50 of the improved model on DroneVehicle data set were increased by 2.4%,1.4%,2.2% respectively. The model also showed good generalization ability on the VisDrone data set.

Uav target detection is a key technology in low altitude security, disaster relief and other fields. However, in practical application scenarios, there are many complex and highly uncertain factors, such as extreme weather changes, large scale and span of the target, complex background interference, motion ambiguity, etc., which makes accurate and real-time UAV target detection still a great challenge. In order to reduce the interference of these situations in real detection scenes and improve the accuracy of UAV detection, a Global Edge Information Enhance (GEIE)module is proposed in this paper, which enables edge information to be fused into features extracted at various scales. It can improve the attention of the network to the edge information of the object. In addition, special weather conditions can greatly reduce the detection accuracy of the target, this paper proposes a Multiscale Edge Feature Enhance(MEFE) module to extract features from different scales and highlight edge information, which can improve the model’s perception of multi-scale features. Finally, we propose a Lightweight layered Shared Convolutional BN(LLSCB) Detection Head based on LSCD, so that the detection heads share the convolutional layer, and the BN is calculated independently, which improves the detection accuracy and reduces the number of parameters. A high performance YOLO detector (YOLO-GML) based on YOLO11 model is proposed. Experimental results show that Compared with YOLO11s, YOLO-GML can improve AP50 by 2.3% to 73.6% on the challenging UAV detection dataset HazyDet, achieving a better balance between accuracy and inference efficiency compared to the most advanced detection algorithms. YOLO-GML also showed good performance improvement in the SODA-A and VisDrone-2019 datasets, demonstrating the generalization of the model.

Small object detection for unmanned aerial vehicle (UAV) image scenarios is a challenging task in the computer vision field. Some problems should be further studied, such as the dense small objects and background noise in high-altitude aerial photography images. To address these issues, an enhanced YOLOv8s-based model for detecting small objects is presented. The proposed model incorporates a parallel multi-scale feature extraction module (PMSE), which enhances the feature extraction capability for small objects by generating adaptive weights with different receptive fields through parallel dilated convolution and deformable convolution, and integrating the generated weight information into shallow feature maps. Then, a scale compensation feature pyramid network (SCFPN) is designed to integrate the spatial feature information derived from the shallow neural network layers with the semantic data extracted from the higher layers of the network, thereby enhancing the network’s capacity for representing features. Furthermore, the largest-object detection layer is removed from the original detection layers, and an ultra-small-object detection layer is applied, with the objective of improving the network’s detection performance for small objects. Finally, the WIOU loss function is employed to balance high- and low-quality samples in the dataset. The results of the experiments conducted on the two public datasets illustrate that the proposed model can enhance the object detection accuracy in UAV image scenarios.

At present, UAV aerial photography has a good prospect in agricultural production, disaster response, and other aspects. The application of UAVs can greatly improve work efficiency and decision-making accuracy. However, owing to inherent features such as a wide field of view and large differences in the target scale in UAV aerial photography images, this can lead to existing target detection algorithms missing small targets or causing incorrect detections. To solve these problems, this paper proposes a small target detection algorithm for UAV aerial photography based on improved YOLOv5s. Firstly, a small target detection layer is applied in the algorithm to improve the detection performance of small targets in aerial images. Secondly, the enhanced weighted bidirectional characteristic pyramid Mul-BiFPN is adopted to replace the PANet network to improve the speed and accuracy of target detection. Then, CIoU was replaced by Focal EIoU to accelerate network convergence and improve regression accuracy. Finally, a non-parametric attention mechanism called the M-SimAM module is added to enhance the feature extraction capability. The proposed algorithm was evaluated on the VisDrone-2019 dataset. Compared with the YOLOV5s, the algorithm improved by 7.30%, 4.60%, 5.60%, and 6.10%, respectively, in mAP@50, mAP@0.5:0.95, the accuracy rate (P), and the recall rate (R). The experiments show that the proposed algorithm has greatly improved performance on small targets compared to YOLOv5s.

Woody vegetation landscape features, such as hedges, tree patches, and riparian vegetation, are important elements of landscape and biotic diversity. For the reason that biodiversity loss is one of the major ecological problems in the EU, it is necessary to establish efficient workflows for the registration and monitoring of woody vegetation landscape features. In the paper, we propose and evaluate a methodology for automated detection of changes in woody vegetation landscape features from a digital orthophoto (DOP). We demonstrate its ability to capture most of the actual changes in the field and thereby provide valuable support for more efficient maintenance of landscape feature layers, which is important for the shaping of future environmental policies. While the most reliable source for vegetation cover mapping is a combination of LiDAR and high-resolution imagery, it can be prohibitively expensive for continuous updates. The DOP from cyclic aerial photography presents an alternative source of up-to-date information for tracking woody vegetation landscape features in-between LiDAR recordings. The proposed methodology uses a segmentation neural network, which is trained with the latest DOP against the last known ground truth as the target. The output is a layer of detected changes, which are validated by the user before being used to update the woody vegetation landscape feature layer. The methodology was tested using the data of a typical traditional Central European cultural landscape, Goričko, in north-eastern Slovenia. The achieved F1 of per-pixel segmentation was 83.5% and 77.1% for two- and five-year differences between the LiDAR-based reference and the DOP, respectively. The validation of the proposed changes at a minimum area threshold of 100 m2 and a minimum area percentage threshold of 20% showed that the model achieved recall close to 90%.

To address the issues of low model accuracy caused by complex ground environments and uneven target scales and high computational complexity in unmanned aerial vehicle (UAV) aerial infrared image target detection, this study proposes a lightweight UAV aerial infrared small target detection algorithm called PHSI-RTDETR. Initially, an improved backbone feature extraction network is designed using the lightweight RPConv-Block module proposed in this paper, which effectively captures small target features, significantly reducing the model complexity and computational burden while improving accuracy. Subsequently, the HiLo attention mechanism is combined with an intra-scale feature interaction module to form an AIFI-HiLo module, which is integrated into a hybrid encoder to enhance the focus of the model on dense targets, reducing the rates of missed and false detections. Moreover, the slimneck-SSFF architecture is introduced as the cross-scale feature fusion architecture of the model, utilizing GSConv and VoVGSCSP modules to enhance adaptability to infrared targets of various scales, producing more semantic information while reducing network computations. Finally, the original GIoU loss is replaced with the Inner-GIoU loss, which uses a scaling factor to control auxiliary bounding boxes to speed up convergence and improve detection accuracy for small targets. The experimental results show that, compared to RT-DETR, PHSI-RTDETR reduces model parameters by 30.55% and floating-point operations by 17.10%. Moreover, detection precision and speed are increased by 3.81% and 13.39%, respectively, and mAP50, impressively, reaches 82.58%, demonstrating the great potential of this model for drone infrared small target detection.

Aerial photography is a long-range, non-contact method of target detection technology that enables qualitative or quantitative analysis of the target. However, aerial photography images generally have certain chromatic aberration and color distortion. Therefore, effective segmentation of aerial images can further enhance the feature information and reduce the computational difficulty for subsequent image processing. In this paper, we propose an improved version of Golden Jackal Optimization, which is dubbed Helper Mechanism Based Golden Jackal Optimization (HGJO), to apply multilevel threshold segmentation to aerial images. The proposed method uses opposition-based learning to boost population diversity. And a new approach to calculate the prey escape energy is proposed to improve the convergence speed of the algorithm. In addition, the Cauchy distribution is introduced to adjust the original update scheme to enhance the exploration capability of the algorithm. Finally, a novel “helper mechanism” is designed to improve the performance for escape the local optima. To demonstrate the effectiveness of the proposed algorithm, we use the CEC2022 benchmark function test suite to perform comparison experiments. the HGJO is compared with the original GJO and five classical meta-heuristics. The experimental results show that HGJO is able to achieve competitive results in the benchmark test set. Finally, all of the algorithms are applied to the experiments of variable threshold segmentation of aerial images, and the results show that the aerial photography images segmented by HGJO beat the others. Noteworthy, the source code of HGJO is publicly available at https://github.com/Vang-z/HGJO .