Development of outdoor swimmers detection system with small object detection method based on deep learning

Xiao, Hanguang; Li, Yuewei; Yu, Xinbing; Xia, Qingling

doi:10.1007/s00530-022-00995-7

Cited by 8 publications

(8 citation statements)

References 35 publications

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“…Xiao H scientific research group proposed an outdoor swimmer object detector, which combined transfer learning and public object data set for training, and evaluated and retrained the model. Experimental data expressed that the total accuracy of this method could reach 90% [9]. Yang X H and other scientists proposed a smart detection and recognition framework with feature weight transfer learning and Yolo v4 network.…”

Section: Related Workmentioning

confidence: 99%

“…Principal component analysis (PCA) is a traditional defect detection method. The calculation of the principal component of the covariance matrix is shown in equation (9).…”

Section: Design Of Add Based On DLmentioning

confidence: 99%

“…In formula (9), the covariance matrix is labeled by C ; the i th standard orthogonal vector is expressed by i v ; its corresponding eigenvalue is indicated by i  . The gap between the input and the training image projections is defined as equation (10).…”

Section: Design Of Add Based On DLmentioning

confidence: 99%

See 2 more Smart Citations

Detection of Apparent Defects in HPLC/Dual Mode Portable Sorting Device Based on Deep Learning and Image Processing

Wang,

Li,

Jiang

et al. 2024

Applied Mathematics and Nonlinear Sciences

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As the growth of automated detection technology, traditional manual detection has gradually been replaced. To improve the effectiveness of defect detection, a HPLC/dual mode portable sorting device with deep learning and image processing is raised for apparent defect detection. The product image is segmented using flood filled mean shift method, and defect detection is achieved using Yolo v3 algorithm. An LME2918 chip is the main component of the communication module in the portable device. Based on experimental data, image segmentation accuracy can range from 80% to 100%, and image matching accuracy can range from 85% to 95%. Under the power line carrier mode, the average success rate of apparent defect detection in portable sorting devices can reach 85%, and the change in success rate is not significant, indicating that the detection is relatively stable; Under wireless communication mode, the average success rate can reach 83%, and the change in success rate is relatively obvious, because wireless communication is easily affected by the external environment. The experimental data shows that the defect detection effect of the HPLC/dual mode portable sorting device based on deep learning and image processing meets the design requirements.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Principal component analysis (PCA) is a traditional defect detection method. The calculation of the principal component of the covariance matrix is shown in equation (9).…”

Section: Design Of Add Based On DLmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Apparent Defects in HPLC/Dual Mode Portable Sorting Device Based on Deep Learning and Image Processing

Wang,

Li,

Jiang

et al. 2024

Applied Mathematics and Nonlinear Sciences

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show abstract

“…Object detection is one of the key research hotspots in computer vision. Relevant researchers have proposed different improvement strategies for the generalized object detection task as well as the small object detection task [22][23][24][25][26].…”

Section: Related Workmentioning

confidence: 99%

Multi-scale feature fusion and lightweight decoupled Head in small object detection

Lv,

Zhang,

Yuan

et al. 2024

Preprint

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Unmanned aerial vehicle (UAV) equipped with high-definition cameras can quickly acquire image data and are widely applied, however, the task suffers from issues such as smaller objects, large scale transformations, dense objects, and occlusion. To address the above issues, LMS-YOLOv7 is proposed by combining multi-scale feature fusion and lightweight decoupled Head. Firstly, we propose the Skipping Fusion Efficient Layer Aggregation Network (SF-ELAN), in which a cross-layer fusion strategy is used to segment, extract and fuse the features in the channel to enhance the feature representation capability. Secondly, based on the multi-scale feature fusion approach, the Multi-scale Cross Stage Partial (MS-CSP) is proposed to fuse deep and shallow information to avoid losing features. In addition, based on our redesigned LightGSConv, the lightweight decoupled detection Head (LD-Head) is proposed, which decouples the localization information of the feature map from the classification information while keeping the detection head lightweight. Extensive experiments on VisDrone and WiderPer-son demonstrate that the proposed method exhibits higher perception of small objects, and the mAP@.5 metric of our method on the VisDrone improves by 3.6% over the baseline method, and our method achieves the highest accuracy on the WiderPerson. The code for the proposed method is publicly available at GitHub.

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“…To solve these problems, the researchers optimize the small object detection method based on various optimization strategies, such as data enhancement [14] , [15] , [16] , [17] , [18] , multi-scale learning [19] , [20] , [21] , [22] , context learning [23] , [24] , [25] , [26] , [27] , and generative confrontation learning [28] , [29] , [30] , [31] , [32] , [33] , [34] , which are analyzed as follows:…”

Section: Introductionmentioning

confidence: 99%

Small object intelligent detection method based on adaptive recursive feature pyramid

Zhang

Liu

et al. 2023

Heliyon

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Development of outdoor swimmers detection system with small object detection method based on deep learning

Cited by 8 publications

References 35 publications

Detection of Apparent Defects in HPLC/Dual Mode Portable Sorting Device Based on Deep Learning and Image Processing

Detection of Apparent Defects in HPLC/Dual Mode Portable Sorting Device Based on Deep Learning and Image Processing

Multi-scale feature fusion and lightweight decoupled Head in small object detection

Small object intelligent detection method based on adaptive recursive feature pyramid

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