Search Results (244)

Wind turbine blades (WTBs) are prone to damage from their working environment, including surface peeling and cracks. Early and effective detection of surface defects on WTBs can avoid complex and costly repairs and serious safety hazards. Traditional object detection methods have disadvantages of insufficient detection capabilities, extended model inference times, low recognition accuracy for small objects, and elongated strip defects within WTB datasets. In light of these challenges, a novel model named DCW-YOLO for surface damage detection of WTBs is proposed in this research, which leverages image data collected by unmanned aerial vehicles (UAVs) and the YOLOv8 algorithm for image analysis. Firstly, Dynamic Separable Convolution (DSConv) is introduced into the C2f module of YOLOv8, allowing the model to more effectively focus on the geometric structural details associated with damage on WTBs. Secondly, the upsampling method is replaced with the content-aware reassembly of features (CARAFE), which significantly minimizes the degradation of image characteristics throughout the upsampling process and boosts the network’s ability to extract features. Finally, the loss function is substituted with the WIoU (Wise-IoU) strategy. This strategy allows for a more accurate regression of the target bounding boxes and helps to improve the reliability in the localization of WTBs damages, especially for low-quality examples. This model demonstrates a notable superiority in surface damage detection of WTBs compared to the original YOLOv8n and has achieved a substantial improvement in the [email protected] metric, rising from 91.4% to 93.8%. Furthermore, in the more rigorous [email protected]–0.95 metric, it has also seen an increase from 68.9% to 71.2%. Full article

In order to test the performance of laser devices for attacking miniature UAVs, we studied the principle of laser devices on soft killing and hard killing. Then, the flight test conditions of miniature UAVs were constructed, and the laser devices were tested and evaluated with the two indexes of maximum jamming range and maximum intercepting range. The first step involves calculating the far-field beam power density corresponding to the unmanned aerial vehicle (UAV) detection equipment and laser device at different distances. Subsequently, the signal electron count received by the UAV detector from the incident laser source target within the integration time tint is computed and compared against the full well charge of the photodetector. This comparison analyzes the UAV detector’s potential for dazzle/blind effects. When the laser device is positioned 600 m from the UAV, the ratio of signal electrons received by the detector to the full well charge was 13.53, indicating that the detector receives signal electrons exceeding the full well charge by over 10 times, thus causing UAV detector blindness. At a distance of 1.2 km from the UAV, this ratio reduces to 2.92, where the detector receives signal electrons around three times the full well charge, causing UAV detector dazzle. Experimental testing determines that the maximum interception distance of this laser device for small, slow-moving UAV equipment is 500 m. Finally, it is proved that the method can effectively test the attacking performance of laser devices, and provides a basis for improving the function and performance of laser devices. Full article

To address the challenge of low detection accuracy and slow detection speed in unmanned aerial vehicle (UAV) aerial images target detection tasks, caused by factors such as complex ground environments, varying UAV flight altitudes and angles, and changes in lighting conditions, this study proposes an end-to-end adaptive multi-scale feature extraction and fusion detection network, named AMFEF-DETR. Specifically, to extract target features from complex backgrounds more accurately, we propose an adaptive backbone network, FADC-ResNet, which dynamically adjusts dilation rates and performs adaptive frequency awareness. This enables the convolutional kernels to effectively adapt to varying scales of ground targets, capturing more details while expanding the receptive field. We also propose a HiLo attention-based intra-scale feature interaction (HLIFI) module to handle high-level features from the backbone. This module uses dual-pathway encoding of high and low frequencies to enhance the focus on the details of dense small targets while reducing noise interference. Additionally, the bidirectional adaptive feature pyramid network (BAFPN) is proposed for cross-scale feature fusion, integrating semantic information and enhancing adaptability. The Inner-Shape-IoU loss function, designed to focus on bounding box shapes and incorporate auxiliary boxes, is introduced to accelerate convergence and improve regression accuracy. When evaluated on the VisDrone dataset, the AMFEF-DETR demonstrated improvements of 4.02% and 16.71% in mAP50 and FPS, respectively, compared to the RT-DETR. Additionally, the AMFEF-DETR model exhibited strong robustness, achieving mAP50 values 2.68% and 3.75% higher than the RT-DETR and YOLOv10, respectively, on the HIT-UAV dataset. Full article

Insulator and defect detection is a critical technology for the automated inspection of transmission and distribution lines within smart grids. However, the development of a lightweight, real-time detection platform suitable for deployment on drones faces significant challenges. These include the high complexity of existing algorithms, limited availability of UAV images, and persistent issues with false positives and missed detections. To address this issue, this paper proposed a lightweight drone-based insulator defect detection method (LDIDD) that integrates data augmentation and attention mechanisms based on YOLOv8. Firstly, to address the limitations of the existing insulator dataset, data augmentation techniques are developed to enhance the diversity and quantity of samples in the dataset. Secondly, to address the issue of the network model’s complexity hindering its application on UAV equipment, depthwise separable convolution is incorporated for lightweight enhancement within the YOLOv8 algorithm framework. Thirdly, a convolutional block attention mechanism is integrated into the feature extraction module to enhance the detection of small insulator targets in aerial images. The experimental results show that the improved network reduces the computational volume by 46.6% and the mAP stably maintains at 98.3% compared to YOLOv8, which enables the implementation of a lightweight insulator defect network suitable for the UAV equipment side without affecting the detection performance. Full article

A lightweight infrared target detection model, G-YOLO, based on an unmanned aerial vehicle (UAV) is proposed to address the issues of low accuracy in target detection of UAV aerial images in complex ground scenarios and large network models that are difficult to apply to mobile or embedded platforms. Firstly, the YOLOv8 backbone feature extraction network is improved and designed based on the lightweight network, GhostBottleneckV2, and the remaining part of the backbone network adopts the depth-separable convolution, DWConv, to replace part of the standard convolution, which effectively retains the detection effect of the model while greatly reducing the number of model parameters and calculations. Secondly, the neck structure is improved by the ODConv module, which adopts an adaptive convolutional structure to adaptively adjust the convolutional kernel size and step size, which allows for more effective feature extraction and detection based on targets at different scales. At the same time, the neck structure is further optimized using the attention mechanism, SEAttention, to improve the model’s ability to learn global information of input feature maps, which is then applied to each channel of each feature map to enhance the useful information in a specific channel and improve the model’s detection performance. Finally, the introduction of the SlideLoss loss function enables the model to calculate the differences between predicted and actual truth bounding boxes during the training process, and adjust the model parameters based on these differences to improve the accuracy and efficiency of object detection. The experimental results show that compared with YOLOv8n, the G-YOLO reduces the missed and false detection rates of infrared small target detection in complex backgrounds. The number of model parameters is reduced by 74.2%, the number of computational floats is reduced by 54.3%, the FPS is improved by 71, which improves the detection efficiency of the model, and the average accuracy (mAP) reaches 91.4%, which verifies the validity of the model for UAV-based infrared small target detection. Furthermore, the FPS of the model reaches 556, and it will be suitable for wider and more complex detection task such as small targets, long-distance targets, and other complex scenes. Full article

In recent years, waste pollution has become a severe threat to riparian environments worldwide. Along with the advancement of deep learning (DL) algorithms (i.e., object detection models), related techniques have become useful for practical applications. This work attempts to develop a data generation approach to generate datasets for small target recognition, especially for recognition in remote sensing images. A relevant point is that similarity between data used for model training and data used for testing is crucially important for object detection model performance. Therefore, obtaining training data with high similarity to the monitored objects is a key objective of this study. Currently, Artificial Intelligence Generated Content (AIGC), such as single target objects generated by Luma AI, is a promising data source for DL-based object detection models. However, most of the training data supporting the generated results are not from Japan. Consequently, the generated data are less similar to monitored objects in Japan, having, for example, different label colors, shapes, and designs. For this study, the authors developed a data generation approach by combining social media (Clean-Up Okayama) and single-image-based 3D model generation algorithms (e.g., InstantMesh) to provide a reliable reference for future generations of localized data. The trained YOLOv8 model in this research, obtained from the S2PS (Similar to Practical Situation) AIGC dataset, produced encouraging results (high F1 scores, approximately 0.9) in scenario-controlled UAV-based riparian PET bottle waste identification tasks. The results of this study show the potential of AIGC to supplement or replace real-world data collection and reduce the on-site work load. Full article

To address the challenges of high model complexity and low accuracy in detecting small targets in insulator defect detection using UAV aerial imagery, we propose a lightweight algorithm, PAL-YOLOv8. Firstly, the baseline model, YOLOv8n, is enhanced by incorporating the PKI Block from PKINet to improve the C2f module, effectively reducing the model complexity and enhancing feature extraction capabilities. Secondly, Adown from YOLOv9 is employed in the backbone and neck for downsampling, which retains more feature information while reducing the feature map size, thus improving the detection accuracy. Additionally, Focaler-SIoU is used as the bounding-box regression loss function to improve model performance by focusing on different regression samples. Finally, pruning is applied to the improved model to further reduce its size. The experimental results show that PAL-YOLOv8 achieves an mAP50 of 95.0%, which represents increases of 5.5% and 2.6% over YOLOv8n and YOLOv9t, respectively. Furthermore, GFLOPs is only 3.9, the model size is just 2.7 MB, and the parameter count is only 1.24 × 10⁶. Full article

To address the larger numbers of small objects and the issues of occlusion and clustering in UAV aerial photography, which can lead to false positives and missed detections, we propose an improved small object detection algorithm for UAV aerial scenarios called YOLOv8 with tiny prediction head and Space-to-Depth Convolution (HSP-YOLOv8). Firstly, a tiny prediction head specifically for small targets is added to provide higher-resolution feature mapping, enabling better predictions. Secondly, we designed the Space-to-Depth Convolution (SPD-Conv) module to mitigate the loss of small target feature information and enhance the robustness of feature information. Lastly, soft non-maximum suppression (Soft-NMS) is used in the post-processing stage to improve accuracy by significantly reducing false positives in the detection results. In experiments on the Visdrone2019 dataset, the improved algorithm increased the detection precision mAP0.5 and mAP0.5:0.95 values by 11% and 9.8%, respectively, compared to the baseline model YOLOv8s. Full article

Road crack detection is of paramount importance for ensuring vehicular traffic safety, and implementing traditional detection methods for cracks inevitably impedes the optimal functioning of traffic. In light of the above, we propose a USSC-YOLO-based target detection algorithm for unmanned aerial vehicle (UAV) road cracks based on machine vision. The algorithm aims to achieve the high-precision detection of road cracks at all scale levels. Compared with the original YOLOv5s, the main improvements to USSC-YOLO are the ShuffleNet V2 block, the coordinate attention (CA) mechanism, and the Swin Transformer. First, to address the problem of large network computational spending, we replace the backbone network of YOLOv5s with ShuffleNet V2 blocks, reducing computational overhead significantly. Next, to reduce the problems caused by the complex background interference, we introduce the CA attention mechanism into the backbone network, which reduces the missed and false detection rate. Finally, we integrate the Swin Transformer block at the end of the neck to enhance the detection accuracy for small target cracks. Experimental results on our self-constructed UAV near–far scene road crack i(UNFSRCI) dataset demonstrate that our model reduces the giga floating-point operations per second (GFLOPs) compared to YOLOv5s while achieving a 6.3% increase in mAP@50 and a 12% improvement in mAP@ [50:95]. This indicates that the model remains lightweight meanwhile providing excellent detection performance. In future work, we will assess road safety conditions based on these detection results to prioritize maintenance sequences for crack targets and facilitate further intelligent management. Full article

In recent years, object detection in unmanned aerial vehicle (UAV) imagery has been a prominent and crucial task, with advancements in drone and remote sensing technologies. However, detecting targets in UAV images pose challenges such as complex background, severe occlusion, dense small targets, and lighting conditions. Despite the notable progress of object detection algorithms based on deep learning, they still struggle with missed detections and false alarms. In this work, we introduce an MCG-RTDETR approach based on the real-time detection transformer (RT-DETR) with dual and deformable convolution modules, a cascaded group attention module, a context-guided feature fusion structure with context-guided downsampling, and a more flexible prediction head for precise object detection in UAV imagery. Experimental outcomes on the VisDrone2019 dataset illustrate that our approach achieves the highest AP of 29.7% and AP50 of 58.2%, surpassing several cutting-edge algorithms. Visual results further validate the model’s robustness and capability in complex environments. Full article

To address the challenges of multi-scale objects, dense distributions, occlusions, and numerous small targets in UAV image detection, we present CMS-YOLOv7, a real-time target detection method based on an enhanced YOLOv7 model. Firstly, the detection layer P2 for small targets was added to YOLOv7 to enhance the detection ability of small and medium-sized targets, and the deep detection head P5 was taken out to mitigate the influence of excessive downsampling on small target images. The anchor frame was calculated by the K-means++ method. Using the concept of Inner-IoU, the Inner-MPDIoU loss function was constructed to control the range of the auxiliary border and improve detection performance. Furthermore, the CARAFE module was introduced to replace traditional upsampling methods, offering improved integration of semantic information during the image upsampling process and enhancing feature mapping accuracy. Simultaneously, during the feature extraction stage, a non-strided convolutional SPD-Conv module was constructed using space-to-depth techniques. This module replaced certain convolutional operations to minimize the loss of fine-grained information and improve the model’s ability to extract features from small targets. Experiments on the UAV aerial photo dataset VisDrone2019 demonstrated that compared with the baseline YOLOv7 object detection algorithm, CMS-YOLOv7 achieved an improvement of 3.5% [email protected], 3.0% [email protected]:0.95, and the number of parameters decreased by 18.54 M. The ability of small target detection was significantly enhanced. Full article

Non-cooperative targets, such as birds and unmanned aerial vehicles (UAVs), are typical low-altitude, slow, and small (LSS) targets with low observability. Radar observations in such scenarios are often complicated by strong motion clutter originating from sources like airplanes and cars. Hence, distinguishing between birds and UAVs in environments with strong motion clutter is crucial for improving target monitoring performance and ensuring flight safety. To address the impact of strong motion clutter on discriminating between UAVs and birds, we propose a frequency correlation dual-SVD (singular value decomposition) reconstruction method. This method exploits the strong power and spectral correlation characteristics of motion clutter, contrasted with the weak scattering characteristics of bird and UAV targets, to effectively suppress clutter. Unlike traditional clutter suppression methods based on SVD, our method avoids residual clutter or target loss while preserving the micro-motion characteristics of the targets. Based on the distinct micro-motion characteristics of birds and UAVs, we extract two key features: the sum of normalized large eigenvalues of the target’s micro-motion component and the energy entropy of the time–frequency spectrum of the radar echoes. Subsequently, the kernel fuzzy c-means algorithm is applied to classify bird and UAV targets. The effectiveness of our proposed method is validated through results using both simulation and experimental data. Full article

Small UAV target detection and tracking based on cross-modality image fusion have gained widespread attention. Due to the limited feature information available from small UAVs in images, where they occupy a minimal number of pixels, the precision required for detection and tracking algorithms is particularly high in complex backgrounds. Image fusion techniques can enrich the detailed information for small UAVs, showing significant advantages under extreme lighting conditions. Image registration is a fundamental step preceding image fusion. It is essential to achieve accurate image alignment before proceeding with image fusion to prevent severe ghosting and artifacts. This paper specifically focused on the alignment of small UAV targets within infrared and visible light imagery. To address this issue, this paper proposed a cross-modality image registration network based on deep learning, which includes a structure preservation and style transformation network (SPSTN) and a multi-level cross-attention residual registration network (MCARN). Firstly, the SPSTN is employed for modality transformation, transferring the cross-modality task into a single-modality task to reduce the information discrepancy between modalities. Then, the MCARN is utilized for single-modality image registration, capable of deeply extracting and fusing features from pseudo infrared and visible images to achieve efficient registration. To validate the effectiveness of the proposed method, comprehensive experimental evaluations were conducted on the Anti-UAV dataset. The extensive evaluation results validate the superiority and universality of the cross-modality image registration framework proposed in this paper, which plays a crucial role in subsequent image fusion tasks for more effective target detection. Full article

The Holy Monastery of Panagia Spiliani is an important religious monument of the Aegean islands. The monastery is built on a steep rocky hill in the Castle of Mandraki on Nisyros island. On the slopes of the foundation area of the monastery, landslides have occurred in the past, mainly rockfalls and slides, while the risk of new similar phenomena in the future is high. To assist the geohazard assessment and mitigation design works, a combined survey using Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle (UAV) photogrammetry was implemented. Besides capturing the detailed morphology within high-resolution 3D point clouds, the main engineering geological units were identified on the slopes, while critical structural ground elements and unstable blocks were mapped in detail. These were quantified in terms of geotechnical parameters, and the engineering geological model of the hill was finalised and presented in an engineering geological map and cross sections. The mitigation measures are targeted towards the stabilisation of the wider area of the upper slope, hence the stability of the monastery and its surroundings risk elements, as well as the support of specific, large- to small-scale unstable rock blocks on the whole slope area, securing accessibility to the main beach of the village. Full article

The core task of target detection is to accurately identify and localize the object of interest from a multitude of interfering factors. This task is particularly difficult in UAV aerial images, where targets are often small and the background can be extremely complex. In response to these challenges, this study introduces an enhanced target detection algorithm for UAV aerial images based on the YOLOv7-tiny network. In order to enhance the convolution module in the backbone of the network, the Receptive Field Coordinate Attention Convolution (RFCAConv) in place of traditional convolution enhances feature extraction within critical image regions. Furthermore, the tiny target detection capability is effectively enhanced by incorporating a tiny object detection layer. Moreover, the newly introduced BSAM attention mechanism dynamically adjusts attention distribution, enabling precise target–background differentiation, particularly in cases of target similarity. Finally, the innovative inner-MPDIoU loss function replaces the CIoU, which enhances the sensitivity of the model to changes in aspect ratio and greatly improves the detection accuracy. Experimental results on the VisDrone2019 dataset reveal that relative to the YOLOv7-tiny model, the improved YOLOv7-tiny model improves precision (P), recall (R), and mean average precision (mAP) by 4.1%, 5.5%, and 6.5%, respectively, thus confirming the algorithm’s superiority over existing mainstream methods. Full article