A small target detection algorithm based on improved YOLOv5 in aerial image.

With the rapid development of optical remote sensing, it is urgent to find a reliable target detection method. Compared with traditional detection algorithms, a convolutional neural network has attracted considerable attention owing to its efficiency and high transitivity. However, different from general images, remote sensing images contain complex background information and dense small targets with changeable directions that make detection very challenging. To solve these problems and provide a stable and high-performance detection method, a rotated saliency fusion object detection (RSD) model based on “you only look once” (YOLO)v4 is established. First, salient image fusion technology is used to magnify target information. Second, the angle variable and rotated non-maximal suppression is introduced to improve the accuracy of rotated object detection by including the detection of dense objects. Third, the network structure is enhanced to improve the performance of small-target detection. Finally, the k-means algorithm and data enhancement are introduced to increase the robustness of the model. Extensive experiments demonstrate the superiority of the proposed model in detection speed and accuracy. The mean average precision of the proposed RSD model reaches 97.32% for the remote sensing images in a harbor area with an average detection speed of 13.41 s − 1.

With the rapid development of UAV and infrared imaging technology, the cost of UAV infrared imaging technology has decreased steadily. Small target detection technology in aerial infrared images has great potential for applications in many fields, especially in the field of traffic surveillance. Because of the low contrast and relatively limited feature information in infrared images compared to visible images, the difficulty involved in small road target detection in infrared aerial images has increased. To solve this problem, this study proposes a feature-enhanced attention and dual-GELAN net (FEADG-net) model. In this network model, the reliability and effectiveness of small target feature extraction is enhanced by a backbone network combined with low-frequency enhancement and a swin transformer. The multi-scale features of the target are fused using a dual-GELAN neck structure, and a detection head with the parameters of the auto-adjusted InnerIoU is constructed to improve the detection accuracy for small infrared targets. The viability of the method was proved using the HIT-UAV dataset and IRTS-AG dataset. According to a comparative experiment, the mAP50 of FEADG-net reached more than 90 percent, which was higher than that of any previous method and it met the real-time requirements. Finally, an ablation experiment was conducted to demonstrate that all three of the modules proposed in the method contributed to the improvement in the detection accuracy. This study not only designs a new algorithm for small road object detection in infrared remote sensing images from UAVs but also provides new ideas for small target detection in remote sensing images for other fields.

Most existing target detection models are designed for image targets in natural scenarios and the detection results of small targets in aerial images are less than satisfactory. This paper tries to solve the locating and classification problem of small targets in aerial images from the perspectives of fusion between shallow features and deep features, receptive field enhancement, and a lightweight feature extraction network. To realize accurate detection of small targets in aerial images, on the basis of SSD (Single Shot MultiBox Detector), an aerial image target detection model MF-SSD based on multi-scale feature fusion and receptive field enhancement is proposed. In this model, a feature fusion sub-network is established, in which, the two-level fusion of shallow features and skip-connection fusion of intermediate features are provided. In addition, a multi-branch Inception structure D-Inception based on dilated convolution is utilized to achieve receptive field enhancement, which enables the system to better extract target features and adapt to the scale change of target. To address the increased computational complexity of the model after feature fusion and receptive field enhancement processing, a lightweight convolutional neural network based on Mobilenet is used to replace the basic network, which can not only reduce the complexity of the network model but also maintain high detection precision. The experimental results show that the target detection model MF-SSD can be used to effectively detect small targets in aerial images.KeywordsSmall target detectionFeature fusionReceptive field enhancementLightweight convolutional neural network (CNN)

The target detection algorithm for accurately locating the location of objects in the image and their categories is widely used in intelligent traffic, forest fire prevention, electric power patrol and other fields. Existing target detection algorithms detect objects in head-up images, and their accuracy is not high when used in aerial image detection. To solve this problem, a small target detection algorithm based on improved RetinaNet model is proposed. First, replace the ResNet101 deep residual network in the original network with ResNet152, Independent of image features, The model is accelerated by merging ScaleNet network structure; Besides, in order to increase the feeling of the small target detection, to upgrade the existing FPN network output, increase the P2 feature layer, improved the range makes the network overall receptive field and the robustness of small target detection. Through the experimental result shows that compared with the original RetinaNet algorithm, The performance index of the improved algorithm is obviously improved. the average detection accuracy of the improved algorithm for small target detection is quite high 6.21%, it has a good detection effect in many scenarios.

In the field of infrared remote sensing, the problem of IR small target detection is still an important component part. Concerning infrared dim small target (IRDST) detection, firstly the infrared image is processed with DWT method to get the wavelet coefficients image, but the distribution characteristics, such as scales, frequencies, orientations of wavelet coefficients in different sub-bands are various, so wavelet image denoised by a single threshold criterion can not give a satisfying estimation. Based on this motivation, a spatially adaptive multi-model de-noising strategy (SAMMDS) based IRDST detection method is proposed in this paper, which can adjust thresholding strategy according to the distribution of noise in different scales and directions. Spatially adaptive BayesShrink (SABS) thresholding, traditional BayesShrink (BS) thresholding and generalized cross validation (GCV) thresholding are all adopted here to process each sub-band separately. After reconstructing the denoised wavelet image, a simple global thresholding is used to separate the background and target finally. Experimental results demonstrate that the proposed algorithm performs better than other typical wavelet methods for small target detection with various complex backgrounds.

: Sea clutter has a non-Rayleigh probability density when examined with a radar that has high resolution. This report discusses the properties of non- Rayleigh sea clutter and the detection of targets in non-Rayleigh sea clutter. The spatially varying clutter model is presented and is used to explain the non- Rayleigh nature of sea clutter. The variation of clutter distributions with various radar parameters such as frequency, pulse width, and polarization was determined, and the correlation properties of clutter were established. A way was developed of constructing a realistic realization of the sea surface on a computer, and the surface was used to predict non-Rayleigh clutter densities for various conditions and to indicate some finer points associated with the detection of small targets on the ocean's surface. Finally, various detectors were compared and the problems of pulse-to-pulse and scan-to-scan processing were analyzed.

At present, deep learning technology has achieved remarkable results in remote sensing target detection, but there are still great challenges to improve the detection accuracy for the complex and changeable background environment in remote sensing images. To solve the above problems, an improved YOLOv3 remote sensing image target detection algorithm is proposed. Firstly, K-means clustering algorithm is used to cluster the data set and cluster the anchors of complex remote sensing targets; Secondly, the loss function is optimized to solve the problem of uneven distribution of difficult and easy samples and improve the ability of the network to extract target features in complex scenes. Finally, the RFB module is introduced to expand the receptive field of the network and strive to improve the missed detection of small targets and dense targets.

In the last decade we have been witnessing the expansive development of the Unmanned Aircraft (UNR) in the world also known as “drones”. Initially, they were developed in the military sector during the World War II, but today they have a mass application for military and civilian goals. The Governments in the World usually use drones for reconnaissance, surveillance, or combined target acquisition and precision strikes, and they also carry out a wider range of tasks for commercial uses including transport in delivery of goods, agriculture, civil infrastructure inspection, search and rescue, aerial images and videos, wireless covering, for leisure use by private individuals, etc. In the Republic of North Macedonia about 1000 UNR are used by civilian personnel and companies;1 only 84 UNR were registered in the Civil Aviation Agency of the Republic of North Macedonia (CAA)2 during the last year. Most of the flights with UNR on the territory of the Republic of North Macedonia are carried out more in urban areas than in rural, and 40% of the flights were made in Skopje from June to December 2018. In the upcoming years, following the world trends, the development of the UNR technology, the relatively low price (from few tens up to a hundred thousand of euros) and the big accessibility, will cause a rapid proliferation in the civil use of UARs in the country. Of course, the large diffusion of UARs raises a series of discussions about the security and privacy of the people and their property and risks to other airspace user. In this paper we will present an overview of UAVs applicability and potential threats in the civil sector in the Republic of North Macedonia. The main source of data are statistical data from CAA. Various contents (study papers, newspaper articles, interviews, guides, regulations, etc.) related to the civil use of UAVs and the threats of it, will be analyzed. Keywords: Unmanned Aircraft (UNR), drone, civil use of UNR, threat

Small and dim target detection is a critical challenge in hyperspectral remote sensing, particularly in complex, large-scale scenes where spectral variability across diverse land cover types complicates the detection process. In this paper, we propose a novel target reasoning algorithm named Adaptive Global Dense Nested Reasoning Network (AGDNR). This algorithm integrates spatial, spectral, and domain knowledge to enhance the detection accuracy of small and dim targets in large-scale environments and simultaneously enables reasoning about target categories. The proposed method involves three key innovations. Firstly, we develop a high-dimensional, multi-layer nested U-Net that facilitates cross-layer feature transfer, preserving high-level features of small and dim targets throughout the network. Secondly, we present a novel approach for computing physicochemical parameters, which enhances the spectral characteristics of targets while minimizing environmental interference. Thirdly, we construct a geographic knowledge graph that incorporates both target and environmental information, enabling global target reasoning and more effective detection of small targets across large-scale scenes. Experimental results on three challenging datasets show that our method outperforms state-of-the-art approaches in detection accuracy and achieves successful classification of different small targets. Consequently, the proposed method offers a robust solution for the precise detection of hyperspectral small targets in large-scale scenarios.

Target detection in wide-band SAR image faces problems with speckle noise and small target detection. This paper uses the DiffusionDet model to solve the problem. DiffusionDet transforms target detection into a denoising diffusion process from noise box to target box, which consists of an image encoder and a detection decoder. When implemented, the pre-trained image encoder is first used to extract SAR image features. Then, the diffusion model is applied to generate candidate positions for target detection boxes on the feature map. By adding noise to the true value box and learning the inversion process, the target box is gradually refined. In the training phase, a SAR image dataset with true value annotations is used for training. Finally, the optimized DiffusionDet model is applied to the wide-band SAR image target detection task, and the detection performance is improved by adjusting parameters. The maximum IoU value of the DiffusionDet model reaches 1, and the average detection time is only 289.325 milliseconds, which is far lower than Faster R-CNN's <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{5 3 4. 1 2 5}$</tex> milliseconds. In addition, the ablation experiment also reveals the trade-off between the number of sampling times and the number of iterations on detection accuracy and inference speed. The DiffusionDet model effectively solves the problem of speckle noise and small target detection in wide-band SAR image target detection, and provides a new method for SAR image target detection.

Unmanned aerial vehicle (UAV) aerial images often present challenges such as small target sizes, high target density, varied shooting angles, and dynamic poses. Existing target detection algorithms exhibit a noticeable performance decline when confronted with UAV aerial images compared to general scenes. This paper proposes an outstanding small target detection algorithm for UAVs, named Fine-Grained Feature Perception YOLOv8s-P2 (FGFP-YOLOv8s-P2), based on YOLOv8s-P2 architecture. We specialize in improving inspection accuracy while meeting real-time inspection requirements. First, we enhance the targets’ pixel information by utilizing slice-assisted training and inference techniques, thereby reducing missed detections. Then, we propose a feature extraction module with deformable convolutions. Decoupling the learning process of offset and modulation scalar enables better adaptation to variations in the size and shape of diverse targets. In addition, we introduce a large kernel spatial pyramid pooling module. By cascading convolutions, we leverage the advantages of large kernels to flexibly adjust the model’s attention to various regions of high-level feature maps, better adapting to complex visual scenes and circumventing the cost drawbacks associated with large kernels. To match the excellent real-time detection performance of the baseline model, we propose an improved Random FasterNet Block. This block introduces randomness during convolution and captures spatial features of non-linear transformation channels, enriching feature representations and enhancing model efficiency. Extensive experiments and comprehensive evaluations on the VisDrone2019 and DOTA-v1.0 datasets demonstrate the effectiveness of FGFP-YOLOv8s-P2. This achievement provides robust technical support for efficient small target detection by UAVs in complex scenarios.

Not just select samples, but exploration[formula omitted] Genetic programming aided remote sensing target detection under deep learning

In recent years, small and weak target detection technology is one of the hotspots in information processing technology. However, the detection precision and speed of weak targets still have yet to be improved. <br/> As a branch of machine learning, deep learning has become more and more widely used in various fields. Therefore, this paper improves the deep convolutional networks for the characteristics of weak target detection, including the following three aspects: <br/> Firstly, a dataset dedicated to small and weak target detection is established. The data is sufficient and representative, which is beneficial to improve the quality of the network model. Each image in the dataset has a corresponding label that indicates the name of the image, and the coordinates and width of the target circumscribed rectangle. <br/> Secondly, the image is dilated many times so that the target having only a few pixels is covered by a lot of pixels. The highlighted portion of the image is dilated, and the result image has a larger highlighted area than the original image. <br/> Thirdly, the Faster R CNN algorithm is improved. In this paper, by adjusting the learning rates, a suitable one is determined to get the best network model. <br/> The results show that the average precision on the dataset has improved. The method proposed in this paper is of great significance for the detection of small and weak targets. For the military field, the research on weak target detection has high military value for improving early warning capability and counterattack capability.

At present, in order to protect the personal safety of riders, electric vehicle riders wearing helmets has become a mandatory requirement of the traffic control department. In order to improve the supervision of cyclists and protect the safety of cyclists. This paper proposes a road helmet detection algorithm based on improved YOLOv5s. Firstly, the SE attention module is embedded in the backbone network of YOLOv5s to solve the problem that small target detection is easy to miss, and secondly, the C3 residual module of the backbone network is replaced by the lightweight ShuffleNetV2 module to reduce the complexity of the network and improve the speed of target detection and identification. Finally, the network is trained and tested on self-made dataset and compared with the original model detection effect. The experimental results show that the detection precision, recall, and mAP0.5 are increased by 4.5%, 3.6, and 4.1%, respectively, after the improvement, and the improved YOLOv5s model can effectively improve the efficiency and timeliness of EV helmet recognition.

Multi-stage morphological operators for small infrared target detection