Anchor-free network with guided attention for ship detection in aerial imagery

Zhang, Sihan; Xin, Ming; Wang, Xile; Zhang, Miaohui

doi:10.1117/1.jrs.15.024511

Cited by 5 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After entering the 21st century, unmanned technology has risen and is being applied rapidly in people's production and life, which brings great convenience and benefits. 1 Like most kinds of mobile robots, the premise for unmanned aerial vehicles (UAVs) to complete the task independently and reliably is the perception of the environment, and the key to the perception of the environment is the target detection technology. 2 Benefiting from the popularity of the deep learning (DL) method in the past decade, the target detection method based on the DL technology has gradually replaced the traditional target detection method and become the first choice in the target detection field because of its strong robustness and adaptability.…”

Section: Introductionmentioning

confidence: 99%

“…After entering the 21st century, unmanned technology has risen and is being applied rapidly in people’s production and life, which brings great convenience and benefits 1 . Like most kinds of mobile robots, the premise for unmanned aerial vehicles (UAVs) to complete the task independently and reliably is the perception of the environment, and the key to the perception of the environment is the target detection technology 2 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Lightweight multi-target detection algorithm for unmanned aerial vehicle aerial imagery

Liu,

Ma,

Wang

2023

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

.Compared with the image captured in the natural scene, the image obtained by unmanned aerial vehicle (UAV) aerial photography has a more complex background and many dense small targets, which puts forward higher requirements for the detection accuracy of the target detection algorithm. However, because the UAV is a kind of small mobile device, how to ensure its real-time detection effect has been a problem. Aiming at these problems, the lightweight YOLOv7 algorithm, namely LRT-YOLOv7, is designed. First, the enhance feature fusion module and the transformer efficient layer aggregation networks module are proposed to improve the performance of feature extraction and fusion to enhance the efficiency of small target detection. Second, aiming at the problems of small target size and complex background in the UAV images, the detection head structure is redesigned in the YOLOv7-tiny algorithm to enhance the multi-scale feature fusion ability of the algorithm and thereby improve the algorithm’s detection accuracy for small targets. Finally, ablation, comparison, and visualization validation experiments were conducted using precision, recall, mean average precision, and frames per second (FPS) as evaluation indicators. The results show that the detection speed of the LRT-YOLOv7 algorithm on the self-made traffic target dataset is 133.8 FPS, and the precision indicator is 84.58%. Therefore, the LRT-YOLOv7 algorithm has high accuracy and real-time performance in traffic target detection tasks for UAV aerial imagery.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lightweight multi-target detection algorithm for unmanned aerial vehicle aerial imagery

Liu,

Ma,

Wang

2023

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

show abstract

“…CFAR has an adaptive threshold detection ability and can adapt to changes in background clutter [13]. It is first applied to detect targets in range profiles.…”

Section: Introductionmentioning

confidence: 99%

“…d xz ≤ r distance of x and z planes d y ≤ h distance of height (13) where: d xz : is the distance of x and z planes; d y : is the distance of height; r: is the cylindrical shape template radius; h: is the cylindrical shape template half the height. Our algorithm starts by randomly searching for pixels in the 3D target point cloud P(x, y, z).…”

mentioning

confidence: 99%

Two-Step CFAR-Based 3D Point Cloud Extraction Method for Circular Scanning Ground-Based Synthetic Aperture Radar

et al. 2023

View full text Add to dashboard Cite

Ground-Based Synthetic Aperture Radar (GBSAR) has non-contact, all-weather, high resolution imaging and microdeformation sensing capabilities, which offers advantages in applications such as building structure monitoring and mine slope deformation retrieval. The Circular Scanning Ground-Based Synthetic Aperture Radar (CS-GBSAR) is one of its newest developed working mode, in which the radar rotates around an axis in a vertical plane. Such nonlinear observation geometry brings the unique advantage of three-dimensional (3D) imaging compared with traditional GBSAR modes. However, such nonlinear observation geometry causes strong sidelobes in SAR images, which makes it a difficult task to extract point cloud data. The Conventional Cell Averaging Constant False Alarm Rate (CA-CFAR) algorithm can extract 3D point cloud data layer-by-layer at different heights, which is time consuming and is easily influenced by strong sidelobes to obtain inaccurate results. To address these problems, this paper proposes a new two-step CFAR-based 3D point cloud extraction method for CS-GBSAR, which can extract accurate 3D point cloud data under the influence of strong sidelobes. It first utilizes maximum projection to obtain three-view images from 3D image data. Then, the first step CA-CFAR is applied to obtain the coarse masks of three-views. Then, the volume mask in the original 3D image is obtained via inverse projection. This can remove strong sidelobes outside the potential target region and obtain potential target area data by intersecting it with the SAR 3D image. Then, the second step CA-CFAR is applied to the potential target area data to obtain 3D point clouds. Finally, to further eliminate the residual strong sidelobes and output accurate 3D point clouds, the modified Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm is applied. The original DBSCAN method uses a spherical template to cluster. It covers more points, which is easily influenced by the strong sidelobe. Hence, the clustering results have more noise points. Meanwhile, modified DBSCAN clusters have a cylindrical template to accommodate the data’s features, which can reduce false clustering. The proposed method is validated via real data acquired by the North China University of Technology (NCUT)-developed CS-GBSAR system. The laser detection and ranging (LiDAR) data are used as the reference ground truth to demonstrate the method. The comparison experiment with conventional method shows that the proposed method can reduce 95.4% false clustered points and remove the strong sidelobes, which shows the better performance of the proposed method.

show abstract

“…The development of technology in recent years has promoted the popularization of UAV in object search tasks. For instance, many teams use the UAV to carry out ship detection [1,2], object tracking [3], and emergency search and rescue [4,5] in the ocean environment. In the field of agriculture, the UAV has a wide range of applications, such as weed recognition [6,7], pest detection [8], and precision pesticide spraying [9], which effectively promotes the intelligentization of agriculture.…”

Section: Introductionmentioning

confidence: 99%

Wide-Area and Real-Time Object Search System of UAV

Ding

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

The method of collecting aerial images or videos by unmanned aerial vehicles (UAVs) for object search has the advantages of high flexibility and low cost, and has been widely used in various fields, such as pipeline inspection, disaster rescue, and forest fire prevention. However, in the case of object search in a wide area, the scanning efficiency and real-time performance of UAV are often difficult to satisfy at the same time, which may lead to missing the best time to perform the task. In this paper, we design a wide-area and real-time object search system of UAV based on deep learning for this problem. The system first solves the problem of area scanning efficiency by controlling the high-resolution camera in order to collect aerial images with a large field of view. For real-time requirements, we adopted three strategies to accelerate the system, as follows: design a parallel system, simplify the object detection algorithm, and use TensorRT on the edge device to optimize the object detection model. We selected the NVIDIA Jetson AGX Xavier edge device as the central processor and verified the feasibility and practicability of the system through the actual application of suspicious vehicle search in the grazing area of the prairie. Experiments have proved that the parallel design of the system can effectively meet the real-time requirements. For the most time-consuming image object detection link, with a slight loss of precision, most algorithms can reach the 400% inference speed of the benchmark in total, after algorithm simplification, and corresponding model’s deployment by TensorRT.

show abstract

Anchor-free network with guided attention for ship detection in aerial imagery

Cited by 5 publications

References 0 publications

Lightweight multi-target detection algorithm for unmanned aerial vehicle aerial imagery

Lightweight multi-target detection algorithm for unmanned aerial vehicle aerial imagery

Two-Step CFAR-Based 3D Point Cloud Extraction Method for Circular Scanning Ground-Based Synthetic Aperture Radar

Wide-Area and Real-Time Object Search System of UAV

Contact Info

Product

Resources

About