Transfer learning based SSD model for helmet and multiple rider detection

Nandhini, C.; Brindha, M.

doi:10.1007/s41870-022-01058-w

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…To ensure the precision of the experiment, we use the same type of test sets in the course of the experiment. We choose some representative models for comparative experiments: Fast Region-based Convolutional Network (Faster-RCNN) [11], Single Shot MultiBox Detector (SSD) [12] and YOLOv5 [13]. The final results of the experiment are displayed in table 3.…”

Section: Results Analysismentioning

confidence: 99%

Construction Site Safety Helmet Wearing Detection Method based on Improved YOLOv5

2023

J. Phys.: Conf. Ser.

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Aiming at real-time monitoring of whether the construction site workers wear helmets correctly according to regulations, we present an improved model which based on the fifth You Only Look Once (YOLOv5) target detection algorithm for helmet wearing detection. Firstly, the clustering algorithm of the network model in the YOLOv5 is optimized to design size of the bounding box. Second, by inducing multi-scale image input to adapt to different image sizes, which enhance the generalization ability of the model. Finally, Complete Intersection over Union (CIoU) instead of original Generalized Intersection over Union (GIoU). CIoU_Loss can entirely consider the distance which are from center point to the length-width ratio between prediction box and ground truth box. The result of final experiment demonstrate that the improved model has a strong identification capability. The detection average precision reach by 92.1%, which can meet can meet the precision demands in actual situations.

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Section: Results Analysismentioning

confidence: 99%

Construction Site Safety Helmet Wearing Detection Method based on Improved YOLOv5

2023

J. Phys.: Conf. Ser.

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“…Image segmentation is a key technology in digital IP that provides a foundation for subsequent OD and other work [16]. In product ADD, it is necessary to segment the product from the background.…”

Section: Design Of Ip Methodsmentioning

confidence: 99%

“…In equation (15), the confidence error is expressed by e Con ; the j th candidate box in the i th grid has no targets and is expressed by nobj ij I ; the sigmoid probability of its existence is marked by ˆj i C , and the classification error calculation is shown in equation (16).…”

Section: Design Of Add Based On DLmentioning

confidence: 99%

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.

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“…In order to ensure that each position of the entire image has one-to-one detection, a large number of dense prior boxes need to be designed. The design method of SSD prior boxes is introduced in detail below 8 . The position of the previous box is represented by the coordinates of the center point and the width and height of the box (cx,cy,w,h), all of which are converted to percentages.…”

Section: The Prior Box Is Generatedmentioning

confidence: 99%

Artificial intelligence volleyball special test system based on deep learning and 3D imaging technology

Li,

Xiong

2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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With the rise of digitalization and intelligence, more and more electronic products begin to replace manual labor, "artificial intelligence volleyball special examination system" is a combination of computer deep learning and three-dimensional imaging technology of college entrance examination service system. The development and application of this system is intended to replace the traditional manual scoring mode, open up a new path for the modern examination mode, and further ensure the fairness of the examination. In this paper, sports literature, experiment, data-driven and other research methods, constantly learn and record volleyball drop point, ball speed and other data in the actual field such as exams and competitions, analyze the data after data collection, and learn and discover patterns, rules and knowledge from the data through training models. Research and development was carried out through data results, and through in-depth calculation, the computer finally reached the point where it could identify various signs in volleyball, volleyball court and volleyball court. The hawk-eye camera could capture the trajectory of volleyball movement in real time without dead corners, and the computer controlled deep learning of volleyball movement images to automatically score the result according to different landing areas. The system through continuous learning, analysis, calculation and other research and development links to achieve accurate prediction effect, and its application in the exam, has achieved accurate points instead of manual, fair and fast, the application of manual system in modern exams has been very necessary, has become an indispensable part of the modern exam.

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Transfer learning based SSD model for helmet and multiple rider detection

Cited by 8 publications

References 25 publications

Construction Site Safety Helmet Wearing Detection Method based on Improved YOLOv5

Construction Site Safety Helmet Wearing Detection Method based on Improved YOLOv5

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

Artificial intelligence volleyball special test system based on deep learning and 3D imaging technology

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