Moving Target Detection Technology Based on UAV Vision

Cheng, Sining; Qin, Jiaxian; Chen, Yuanyuan; Li, Mingzhu

doi:10.1155/2022/5443237

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…Yang uses a background subtraction algorithm to detect and recognize moving targets [16]. Besides, the Yolov3 algorithm is put forward in the process of machine vision object detection, and the expected recognition effect is basically achieved [17]. In parallel, Michael et al, "introduced the training of a deep learning detector for better motion recognition [18].…”

Section: Related Workmentioning

confidence: 99%

Research on detecting moving targets with an improved Kalman filter algorithm

2023

KSII TIIS

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As science and technology evolve, object detection of moving objects has been widely used in the context of machine learning and artificial intelligence. Traditional moving object detection algorithms, however, are characterized by relatively poor real-time performance and low accuracy in detecting moving objects. To tackle this issue, this manuscript proposes a modified Kalman filter algorithm, which aims to expand the equations of the system with the Taylor series first, ignoring the higher order terms of the second order and above, when the nonlinear system is close to the linear form, then it uses standard Kalman filter algorithms to measure the situation of the system. which can not only detect moving objects accurately but also has better real-time performance and can be employed to predict the trajectory of moving objects. Meanwhile, the accuracy and real-time performance of the algorithm were experimentally verified.

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Section: Related Workmentioning

confidence: 99%

Research on detecting moving targets with an improved Kalman filter algorithm

2023

KSII TIIS

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“…By incorporating detection techniques into UAVs, tasks such as traffic monitoring, urban planning [ 6 ], and resource management [ 7 ] can be effectively accomplished, offering immense practical value. The integration of object detection and UAV technology has become a hot research topic among researchers [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

YOLOv5s-DSD: An Improved Aerial Image Detection Algorithm Based on YOLOv5s

Sun

Chen

Xiao

et al. 2023

Sensors

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Due to the challenges of small detection targets, dense target distribution, and complex backgrounds in aerial images, existing object detection algorithms perform poorly in aerial image detection tasks. To address these issues, this paper proposes an improved algorithm called YOLOv5s-DSD based on YOLOv5s. Specifically, the SPDA-C3 structure is proposed and used to reduce information loss while focusing on useful features, effectively tackling the challenges of small detection targets and complex backgrounds. The novel decoupled head structure, Res-DHead, is introduced, along with an additional small object detection head, further improving the network’s performance in detecting small objects. The original NMS is replaced by Soft-NMS-CIOU to address the issue of neighboring box suppression caused by dense object distribution. Finally, extensive ablation experiments and comparative tests are conducted on the VisDrone2019 dataset, and the results demonstrate that YOLOv5s-DSD outperforms current state-of-the-art object detection models in aerial image detection tasks. The proposed improved algorithm achieves a significant improvement compared with the original algorithm, with an increase of 17.4% in mAP@0.5 and 16.4% in mAP@0.5:0.95, validating the superiority of the proposed improvements.

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“…Mainly, the following drawbacks exist: a large number of redundant regions are generated along with candidate regions; it is difficult to extract semantic informationrich regions from complex images [19]. Therefore, for some scenes with complex backgrounds, uneven illumination, and small defects, traditional target detection algorithms [20] suffer from missed and false detection, resulting in low detection accuracy, poor real-time performance, and weak generalization ability.…”

Section: Introductionmentioning

confidence: 99%

TAFFNet: Two-Stage Attention-Based Feature Fusion Network for Surface Defect Detection

Cao

Yang

2022

J Sign Process Syst

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In recent years, deep learning has made significant progress in wood panel defect detection. However, there are still challenges such as low detection , slow detection speed, and difficulties in deploying embedded devices on wood panel surfaces. To overcome these issues, we propose a lightweight wood panel defect detection method called YOLOv5-LW, which incorporates attention mechanisms and a feature fusion network. Firstly, to enhance the detection capability of acceptable defects, we introduce the Multi-scale Bi-directional Feature Pyramid Network (MBiFPN) as a feature fusion network. The MBiFPN reduces feature loss, enriches local and detailed features, and improves the model's detection capability for acceptable defects. Secondly, to achieve a lightweight design, we reconstruct the ShuffleNetv2 network model as the backbone network. This reconstruction reduces the number of parameters and computational requirements while maintaining performance. We also introduce the Stem Block and Spatial Pyramid Pooling Fast (SPPF) models to compensate for any accuracy loss resulting from the lightweight design, ensuring the model's detection capabilities remain intact while being computationally efficient. Thirdly, we enhance the backbone network by incorporating Efficient Channel Attention (ECA), which improves the network's focus on key information relevant to defect detection. By attending to essential features, the model becomes more proficient in accurately identifying and localizing defects. We validate the proposed method using a selfdeveloped wood panel defect dataset. The experimental results demonstrate the effectiveness of the improved YOLOv5-LW method. Compared to the original model, our approach achieves a 92.8% accuracy rate, reduces the number of parameters by 27.78%, compresses computational volume by 41.25%, improves detection inference speed by 10.16%, and enhances the detection accuracy of two types of acceptable defects (dead knots and cracks) by 0.2% and 1.3% respectively.

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Moving Target Detection Technology Based on UAV Vision

Cited by 7 publications

References 27 publications

Research on detecting moving targets with an improved Kalman filter algorithm

Research on detecting moving targets with an improved Kalman filter algorithm

YOLOv5s-DSD: An Improved Aerial Image Detection Algorithm Based on YOLOv5s

TAFFNet: Two-Stage Attention-Based Feature Fusion Network for Surface Defect Detection

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