Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

Fan, Jiwei; Yang, Xiaogang; Lu, Richard; Xie, Xueli; Li, Weipeng

doi:10.3390/drones5030068

Cited by 12 publications

(9 citation statements)

References 40 publications

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“…Because the current experimental new method of the Sun Yat-Sen University team is the first to propose RGBT-CC and use the dataset to complete the comparison of different fusion models, on the basis of this scheme, our method is improved and compared with experiments. We still use classical counting models such as MCNN [33], SANet [69], CSRNet [33], and Bayesian Loss [70] as the backbone networks for experimental reference. Compared with the best result of Bayesian Loss [70], our model has improved MAE and MSE by 0.54 and 0.53 on the RGBT-CC dataset.…”

Section: Density Regression Methodmentioning

confidence: 99%

“…Similarly, our method is compared with multiple best-performing multimodal fusion models of UCNet [66], HDFNet [67], and BBSNet [68], and compared with the best-performing BBSNet [7], it is found that our model has 1.4 and 0.34 improvement in MAE and MSE on the RGBT-CC dataset. In the literature of the Sun Yat-Sen University team, the integration of the IADM "early fusion" mechanism into the classical counting model networks such as MCNN [33], SANet [69], CSRNet [33], and Bayesian Loss [70] can improve the performance of the model. e MAE and MSE of MCNN + IADM [33] have an improvement of 2.12 and 2.14; the MAE and MSE of SANet + IADM [33] have an improvement of 3.81 and 7.88; the MAE and MSE of CSRNet + IADM [33] have an improvement of 2.56 and 4.35; and the MAE and MSE of Bayesian Loss + IADM [33] have a 3.09 and 4.49 improvement.…”

Section: Density Regression Methodmentioning

confidence: 99%

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Research on 24-Hour Dense Crowd Counting and Object Detection System Based on Multimodal Image Optimization Feature Fusion

Ren

2022

Scientific Programming

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Motivation. In the environment of day and night video surveillance, in order to improve the accuracy of machine vision dense crowd counting and target detection, this paper designs a day and night dual-purpose crowd counting and crowd detection network based on multimode image fusion. Methods. Two sub-models, RGBD-Net and RGBT-Net, are designed in this paper. The depth image features and thermal imaging features are effectively fused with the features of visible light images, so that the model has stronger anti-interference characteristics and robustness to the light noise interference caused by the sudden fall of light at night. The above models use density map regression-guided detection method to complete population counting and detection. Results. The model completed daytime training and testing on MICC dataset. Through verification, the average absolute error of the model was 1.025, the mean square error was 1.521, and the recall rate of target detection was 97.11%. Night vision training and testing were completed on the RGBT-CC dataset. After verification, the average absolute error of the network was 18.16, the mean square error was 32.14, and the recall rate of target detection was 97.65%. By verifying the effectiveness of the multimode medium-term fusion network, it is found to exceed the current most advanced bimodal fusion method. Conclusion. The experimental results show that the proposed multimodal fusion network can solve the counting and detection problem in the video surveillance environment during day and night. The ablation experiment further proves the effectiveness of the parameters of the two models.

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Section: Density Regression Methodmentioning

confidence: 99%

Section: Density Regression Methodmentioning

confidence: 99%

Research on 24-Hour Dense Crowd Counting and Object Detection System Based on Multimodal Image Optimization Feature Fusion

Ren

2022

Scientific Programming

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“…NMANNED aerial vehicles (UAVs) have emerged as versatile and efficient tools in various application domains, such as aerial surveillance [1], target tracking [2], [3], and disaster response [4], [5]. A pivotal task within UAV systems is geo-localization, which estimates the geographic coordinates of drones in real time.…”

Section: Introductionmentioning

confidence: 99%

GeoFormer: An Effective Transformer-Based Siamese Network for UAV Geolocalization

Li,

Yang,

Fan

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Cross-view geo-localization of unmanned aerial vehicles (UAVs) is a challenging task due to the positional discrepancies and uncertainties in scale and distance between UAVs and satellite views. Existing transformer-based geolocalization methods mainly use encoders to mine image contextual information. However, these methods have some limitations when dealing with scale changes in cross-view images. Therefore, we present an effective transformer-based Siamese network tailored for UAV geo-localization, called GeoFormer. Firstly, an efficient transformer feature extraction network was designed, which utilizes linear attention to reduce the computational complexity and improve the computational efficiency of the network. Among them, we designed an efficient separable perceptron module based on depth-wise separable convolution, which can effectively reduce the computational cost while improving the feature representation of the network. Secondly, we proposed a multi-scale feature aggregation module (MFAM), which deeply fuses salient features at different scales through a feed-forward neural network to generate global feature representations with rich semantics, which improves the model's ability to capture image details and represent robust features. Additionally, we designed a semantic-guided region segmentation module (SRSM), which utilizes a k-modes clustering algorithm to divide the feature map into multiple regions with semantic consistency and performs feature recognition within each semantic region to improve the accuracy of image matching. Finally, we designed a hierarchical reinforcement rotation matching strategy to achieve accurate UAV geo-localization based on the retrieval results of UAV view query satellite images using SuperPoint keypoints extraction and LightGlue rotation matching. According to the experimental results, our method effectively achieves UAV geo-localization.

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“…However, the majority of research has focused on tracking objects in fixed or horizontally moving camera settings, such as handheld or vehicle-mounted cameras, with limited perceptual range. In recent years, unmanned aerial vehicles (UAVs) have gained widespread popularity in various domains, such as search and rescue, agriculture, sports analysis, and geographical surveying [6][7][8][9][10]. Multi-object tracking in airborne camera views faces more complex challenges than with fixed or horizontally moving cameras, such as small target sizes and fast camera movements [11,12].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Online UAV Multi-Object Tracking with Temporal Context and Spatial Topological Relationships

Xiao

Cao

Zhong³

et al. 2023

Drones

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Multi-object tracking in unmanned aerial vehicle (UAV) videos is a critical visual perception task with numerous applications. However, existing multi-object tracking methods, when directly applied to UAV scenarios, face significant challenges in maintaining robust tracking due to factors such as motion blur and small object sizes. Additionally, existing UAV methods tend to underutilize crucial information from the temporal and spatial dimensions. To address these issues, on the one hand, we propose a temporal feature aggregation module (TFAM), which effectively combines temporal contexts to obtain rich feature response maps in dynamic motion scenes to enhance the detection capability of the proposed tracker. On the other hand, we introduce a topology-integrated embedding module (TIEM) that captures the topological relationships between objects and their surrounding environment globally and sparsely, thereby integrating spatial layout information. The proposed TIEM significantly enhances the discriminative power of object embedding features, resulting in more precise data association. By integrating these two carefully designed modules into a one-stage online MOT system, we construct a robust UAV tracker. Compared to the baseline approach, the proposed model demonstrates significant improvements in MOTA on two UAV multi-object tracking benchmarks, namely VisDrone2019 and UAVDT. Specifically, the proposed model achieves a 2.2% improvement in MOTA on the VisDrone2019 benchmark and a 2.5% improvement on the UAVDT benchmark.

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Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

Cited by 12 publications

References 40 publications

Research on 24-Hour Dense Crowd Counting and Object Detection System Based on Multimodal Image Optimization Feature Fusion

Research on 24-Hour Dense Crowd Counting and Object Detection System Based on Multimodal Image Optimization Feature Fusion

GeoFormer: An Effective Transformer-Based Siamese Network for UAV Geolocalization

Enhancing Online UAV Multi-Object Tracking with Temporal Context and Spatial Topological Relationships

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