Small aircraft detection using deep learning

Kiyak, Emre; Unal, Gulay

doi:10.1108/aeat-11-2020-0259

Cited by 15 publications

(7 citation statements)

References 23 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By centering the object in the image where radial distortion is smaller and also by limiting the noise due to vibrations [13], gimbals can be used successfully in active object tracking applications. Even though several high-performance visual tracking algorithms exist [14][15][16], many are computationally demanding and rely on parallel processing units [17,18], thus being unsuited for the limited on-board computation capabilities of small UAVs [19].…”

Section: Active Tracking By Viewpoint Controlmentioning

confidence: 99%

Active Object Detection and Tracking Using Gimbal Mechanisms for Autonomous Drone Applications

Hansen,

de Figueiredo

2024

Drones

View full text Add to dashboard Cite

Object recognition, localization, and tracking play a role of primordial importance in computer vision applications. However, it is still an extremely difficult task, particularly in scenarios where objects are attended to using fast-moving UAVs that need to robustly operate in real time. Typically the performance of these vision-based systems is affected by motion blur and geometric distortions, to name but two issues. Gimbal systems are thus essential to compensate for motion blur and ensure visual streams are stable. In this work, we investigate the advantages of active tracking approaches using a three-degrees-of-freedom (DoF) gimbal system mounted on UAVs. A method that utilizes joint movement and visual information for actively tracking spherical and planar objects in real time is proposed. Tracking methodologies are tested and evaluated in two different realistic Gazebo simulation environments: the first on 3D positional tracking (sphere) and the second on tracking of 6D poses (planar fiducial markers). We show that active object tracking is advantageous for UAV applications, first, by reducing motion blur, caused by fast camera motion and vibrations, and, second, by fixating the object of interest within the center of the field of view and thus reducing re-projection errors due to peripheral distortion. The results demonstrate significant object pose estimation accuracy improvements of active approaches when compared with traditional passive ones. More specifically, a set of experiments suggests that active gimbal tracking can increase the spatial estimation accuracy of known-size moving objects, under conditions of challenging motion patterns and in the presence of image distortion.

show abstract

Section: Active Tracking By Viewpoint Controlmentioning

confidence: 99%

Active Object Detection and Tracking Using Gimbal Mechanisms for Autonomous Drone Applications

Hansen,

de Figueiredo

2024

Drones

View full text Add to dashboard Cite

show abstract

“…It is necessary to use as much information and as many characteristics as possible from the collected point cloud data. The regression of the 3D bounding box is fine-tuned in the second stage of the network [48]. The problem with anchor-free detection based on the centerpoint is that there is not enough spatial information for accurate positioning, and it does not take full advantage of the 3D characteristics of objects for accurate identification.…”

Section: Attentional Feature Fusion Refinement Modulementioning

confidence: 99%

AFRNet: Anchor-Free Object Detection Using Roadside LiDAR in Urban Scenes

Wang,

Lan,

2024

Remote Sensing

View full text Add to dashboard Cite

In urban settings, roadside infrastructure LiDAR is a ground-based remote sensing system that collects 3D sparse point clouds for the traffic object detection of vehicles, pedestrians, and cyclists. Current anchor-free algorithms for 3D point cloud object detection based on roadside infrastructure face challenges related to inadequate feature extraction, disregard for spatial information in large 3D scenes, and inaccurate object detection. In this study, we propose AFRNet, a two-stage anchor-free detection network, to address the aforementioned challenges. We propose a 3D feature extraction backbone based on the large sparse kernel convolution (LSKC) feature set abstraction module, and incorporate the CBAM attention mechanism to enhance the large scene feature extraction capability and the representation of the point cloud features, enabling the network to prioritize the object of interest. After completing the first stage of center-based prediction, we propose a refinement method based on attentional feature fusion, where fused features incorporating raw point cloud features, voxel features, BEV features, and key point features are used for the second stage of refinement to complete the detection of 3D objects. To evaluate the performance of our detection algorithms, we conducted experiments using roadside LiDAR data from the urban traffic dataset DAIR-V2X, based on the Beijing High-Level Automated Driving Demonstration Area. The experimental results show that AFRNet has an average of 5.27 percent higher detection accuracy than CenterPoint for traffic objects. Comparative tests further confirm that our method achieves high accuracy in roadside LiDAR object detection.

show abstract

“…After the introduction of “Made in China 2025”, all kinds of enterprises in the manufacturing industry speed up the pace of informatization and modernization to realize intelligence and digitalization. This helps develop enterprises rapidly and improves product quality and work efficiency (Emre & Gulary, 2021), promoting economic development and the quality growth of products. However, IMS in Chinese enterprises is not mature enough.…”

Section: Introductionmentioning

confidence: 99%

The evaluation of enterprise supply chain intelligent manufacturing system for agricultural interconnection data based on machine learning

Yu¹,

Li²,

Li³

et al. 2023

Expert Systems

View full text Add to dashboard Cite

The present work aims to promote the modernization of the real economy and facilitate the manufacturing industry to construct an intelligent manufacturing system. Analysis and evaluation are performed on the intelligent manufacturing system (IMS) of enterprises under the Internet of Things (IoT). First, the connotation and characteristics of machine learning (ML) in the era of IoT are explained, followed by the introduction to neural networks. Second, the optimization process of the neural network is described. Since it has no memory, the traditional deep neural network (DNN) needs to be improved. Then, the Generative Adversarial Network (GAN) is utilized to expand the sample data. Moreover, the confusion matrix is used in the indicator evaluation. The backpropagation neural network (BPNN) is optimized by the genetic algorithm (GA) to improve the network configuration and model performance. In addition, the connotation and development status of IMS are analysed, and the feasibility of applying deep learning to the evaluation of IMS is discussed. Finally, the evaluation model of IMS is designed and built via a neural network, which is ultimately verified in the simulation experiment. The experimental results indicate that the BPNN‐GA algorithm has advantages in predicting fitting accuracy, taking up less memory, and shortening training time; it can achieve generalization and accuracy in the forefront of optimal Pareto value, and the fitting accuracy attains 95.3%. The horizontal evaluation of IMS proves the effectiveness and feasibility of the evaluation model. This shows that the BPNN‐GA algorithm has an excellent evaluation effect on IMS. And the IMS evaluation model is applied to assess the IMS of sample enterprises. The evaluation results demonstrate that there are problems in the current manufacturing industry. For example, the transactions are challenging to be completed smoothly, information technology is updated slowly, and IMS is dependent on collecting resources.

show abstract

Small aircraft detection using deep learning

Cited by 15 publications

References 23 publications

Active Object Detection and Tracking Using Gimbal Mechanisms for Autonomous Drone Applications

Active Object Detection and Tracking Using Gimbal Mechanisms for Autonomous Drone Applications

AFRNet: Anchor-Free Object Detection Using Roadside LiDAR in Urban Scenes

The evaluation of enterprise supply chain intelligent manufacturing system for agricultural interconnection data based on machine learning

Contact Info

Product

Resources

About