The effect of downsampling–upsampling strategy on foreground detection algorithms

Molina‐Cabello, Miguel A.; García‐González, Jorge; Luque‐Baena, Rafael Marcos; López‐Rubio, Ezequiel

doi:10.1007/s10462-020-09811-y

Cited by 5 publications

(3 citation statements)

References 43 publications

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“…Therefore, the model cannot improve the prediction accuracy of the temperature of common steel at the expense of special steel. At present, for the problem of uneven data distribution, the main treatment schemes include downsampling and oversampling [9][10]. Over-sampling processing is not applicable to steel mill data, as the production data of common steel types are relatively large.…”

Section: Uneven Distribution Of Datamentioning

confidence: 99%

Improvement of the model by preprocessing big data of tapping temperature prediction industry

Dong

2022

J. Phys.: Conf. Ser.

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The quality of steel is highly related to the tapping temperature. At present, many models can predict the tapping temperature to a certain extent. However, after in-depth exploration of the model, it was found that most of the models have a better performance for the tapping temperature prediction of common steel, while the special steel was ignored. It is because that most models are rough in the data processing. The neglect of incomplete data, uneven distribution problems have caused models to over-emphasize some types of steel, while it does not agree with the production demand of steel mills. This paper proposes Bayes neural network model with the improved filling method based on K-Means algorithm and data homogenization method based on K-Means algorithm. The statistical results show that the model has a better performance at error within ±5°C ratio, mean absolute error, root means square error, etc. than the traditional ones.

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Section: Uneven Distribution Of Datamentioning

confidence: 99%

Improvement of the model by preprocessing big data of tapping temperature prediction industry

Dong

2022

J. Phys.: Conf. Ser.

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“…To surmount these challenges and significantly augment the efficiency of the model within the unique context of the aquaculture industry, a holistic suite of technical enhancements has been carefully curated and implemented. These advancements span the introduction of an innovative downsampling technique designed [5][6][7][8] to preserve pivotal image features, a thorough optimization of the model's structural backbone and neck to facilitate superior feature processing capabilities, and the recalibration of loss functions [9][10][11] to more accurately reflect the distinctive attributes of images captured by drones. Furthermore, the establishment of branches capable of high-resolution detection enables the precise identification of minuscule objects.…”

Section: Introductionmentioning

confidence: 99%

Deploying a Computer Vision Model Based on YOLOv8 Suitable for Drones in the Tuna Fishing and Aquaculture Industry

Pham,

Han

2024

JMSE

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In recent years, the global tuna fishing and aquaculture industry has encountered significant challenges in balancing operational efficiency with sustainable resource management. This study introduces an innovative approach utilizing an advanced computer vision model, PA-YOLOv8, specifically adapted for drones, to enhance the monitoring and management of tuna populations. PA-YOLOv8 leverages the capabilities of YOLOv8, a state-of-the-art object detection system known for its precision and speed, tailored to address the unique demands of aerial surveillance in marine environments. Through comprehensive modifications including downsampling techniques, feature fusion enhancements, and the integration of the Global Attention Module (GAM), the model significantly improves the detection accuracy of small and juvenile tuna within complex aquatic landscapes. Experimental results using the Tuna dataset from Roboflow demonstrate marked improvements in detection metrics such as precision, recall, and mean average precision (mAP), affirming the model’s effectiveness. This study underscores the potential of integrating cutting-edge technologies like UAVs and computer vision in promoting sustainable practices in the aquaculture sector, setting a new standard for technological applications in environmental and resource management. The advancements presented here provide a scalable and efficient solution for real-time monitoring, contributing to the long-term sustainability of marine ecosystems.

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“…The standard methods for downsampling or upsampling are decimation or duplication and bi-linear interpolation. Several studies have applied bi-linear or bi-cubic interpolation for upsampling and downsampling [9].…”

Section: Introductionmentioning

confidence: 99%

Echocardiogram Image Quality Enhancement using Upsampling and Histogram Matching Methods

Permana,

Puspasari

2023

Jurnal Teknik Elektro

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The prevalence of heart disease has been increasing in the last ten years. One of the cardiac diagnostic tools is echocardiography. Echocardiogram medical images provide essential information, including shape, size, pumping capacity, heart function abnormalities, and location of heart damage, but echocardiogram images have high noise content and poor contrast, as well as limitations due to differences in anatomy or body mass. This will affect the reading results of patient diagnosis. Therefore, image quality improvement is needed by removing noise and increasing image contrast. This research has improved image quality using a method with low mathematical complexity and a fast computational process. The method used is the Upsampling method to generate a reference image. The quality of the image produced was the Nearest Neighbor upsampling method: 2.8 dB, Bi-linear Interpolation: 2.78 dB, and Bi-cubic Interpolation: 2.73 dB. Furthermore, the image with the highest SNR value is processed with Histogram Matching to accelerate improving image quality. The Histogram Matching image increases quality by more than 50% with a SSIM value of 0.54. The required computational process to apply this method to each medical image has an average duration of 0.4 s. This result provides a higher value than several methods using linear scaling and speckle reducing.

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The effect of downsampling–upsampling strategy on foreground detection algorithms

Cited by 5 publications

References 43 publications

Improvement of the model by preprocessing big data of tapping temperature prediction industry

Improvement of the model by preprocessing big data of tapping temperature prediction industry

Deploying a Computer Vision Model Based on YOLOv8 Suitable for Drones in the Tuna Fishing and Aquaculture Industry

Echocardiogram Image Quality Enhancement using Upsampling and Histogram Matching Methods

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