Study of Using Deep Learning Nets for Mark Detection in Space Docking Control Images

Fomin, Ivan; Bakhshiev, Aleksandr; Gromoshinskii, Dmitrii

doi:10.1016/j.procs.2017.01.011

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…The following performance metrics [20] are used to evaluate the performance of the algorithm in this paper: accuracy (P, precision), recall (R, recall), and mean average precision (mAP, mean average precision).…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

YOLOv5-based fatigue state detection method

2022

AJCIS

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With the development of social economy and the continuous increase of car ownership in China, fatigue driving is one of the problems people need to focus on nowadays, however, most of the current fatigue state detection methods are affected by the problems of expensive, susceptible to the environment and complicated methods. In order to solve the above problems, this paper proposes a fatigue state detection method with YOLOv5m as the basic network model, which first enhances the original image and then improves the loss function. The experimental results show that the mean accuracy of this method can be as high as 95.6%, which is 4.13 percentage points higher than YOLOv4 and 6.2 percentage points higher than YOLOv3, and the model accuracy is as high as 98.27%, which is 4 and 5.2 percentage points higher than YOLOv4 and YOLOv3, respectively. The recall rate is 95.1%, which is 2 and 3.2 percentage points higher than YOLOv4 and YOLOv3, respectively. It proves the reliability and advantages of the method in this paper.

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Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

YOLOv5-based fatigue state detection method

2022

AJCIS

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“…With new tools to analyse images at a level never known before, many scientific fields are experiencing a major shift in automated information and pattern extraction from images and videos. Once again, the medical field is interesting to look at, but many examples can be found for self-driving cars, spacecraft docking, etc [32][33][34][35].…”

Section: Easier Image Metrologymentioning

confidence: 99%

Pragmatic Big Data and smart manufacturing

Benhenni¹

2017

18th International Congress of Metrology

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Abstract. Smart manufacturing is usually associated with fancy cobots and automated production lines. But the Big Data ecosystem has a part to play, and is probably faster and cheaper to start with. The numerical revolution has dramatically accelerated these last years, with many implications in different industries. We propose in this article to present these new tools, and discuss some real use cases. We will also present the best practices and some of the inevitable challenges related to data projects.The upcoming Industry 4.0 movement is full of promises. However, many still think it is the vision of a distant future, implying heavy changes and new fancy but expensive machines and tools. But it is first and foremost a digital transformation, and as such, will generate its share of data. It will therefore be possible to leverage the Big Data technological ecosystem to: -harness and bring value out of the data, -improve quality and conformance, -automate controls on the production line, -have a tight control on industrial and manufacturing processes as well as on the machinery and tools.

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“…At present, the detection model is mainly divided into a single-stage model and a two-stage model, such as YOLO, SSD, and Mask R-CNN [16][17][18][19][20][21]. Zeng and Xia [22] proposed a space target recognition method based on the DCNN and [23] proposed a kind of target feature point extraction method based on deep learning to realise the detection and location determination of a docking node.…”

Section: Introductionmentioning

confidence: 99%

R-CNN-Based Satellite Components Detection in Optical Images

Chen

Gao

Zhang³

2020

International Journal of Aerospace Engineering

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The accurate detection of satellite components based on optical images can provide data support for aerospace missions such as pointing and tracking between satellites. However, the traditional target detection method is inefficient when performing calculations and has a low detection precision, especially when the attitude of the satellite and illumination conditions change considerably. To enable the precise detection of satellite components, we analyse the imaging characteristics of a satellite in space and propose a method to detect the satellite components. This approach is based on a regional-based convolutional neural network (R-CNN), and it can enable the accurate detection of various satellite components by using optical images. First, on the basis of the Mask R-CNN, we combine the DenseNet, ResNet, and FPN to construct a new feature extraction structure and obtain the R-CNN based satellite-component-detection model (RSD). The feature maps are extracted and concatenated at a deeper multiscale level, and the feature propagation between each layer is enhanced by providing a dense connection. Next, an information-rich satellite dataset is constructed, which is composed of images of various kinds of satellites from various perspectives and orbital positions. The detection model is trained and optimized on the constructed dataset to obtain the satellite component detection model. Finally, the proposed RSD model and original Mask R-CNN are tested on the same established test set. The experimental results show that the proposed detection model has higher precision, recall rate, and F1 score. Therefore, the proposed approach can effectively detect satellite components, based on optical images.

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Study of Using Deep Learning Nets for Mark Detection in Space Docking Control Images

Cited by 8 publications

References 8 publications

YOLOv5-based fatigue state detection method

YOLOv5-based fatigue state detection method

Pragmatic Big Data and smart manufacturing

R-CNN-Based Satellite Components Detection in Optical Images

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