Automatic Target Detection from Satellite Imagery Using Machine Learning

Tahir, Arsalan; Munawar, Hafiz Suliman; Akram, Junaid; Adil, Muhammad; Ali, Shehryar; Kouzani, Abbas Z.; Mahmud, Arafat

doi:10.3390/s22031147

Cited by 40 publications

(12 citation statements)

References 90 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among them, the YOLO ( 27) series and single-shot detector (SSD) (45) are typical representations of one-stage detection, and the faster region-based convolutional neural network (faster-RCNN) ( 46) is a typical representation of a two-stage detection model. Tahir et al previously reported that the faster-RCNN had a higher accuracy (95.31%) than YOLO (94.2%) and SSD (84.61%) in satellite imagery to detect objects; however, YOLO is an obvious leader in terms of speed and efficiency (47). Alkentar et al indicated that SSD had good detection ability but a high false positive ratio for drone detection.…”

Section: Discussionmentioning

confidence: 99%

Fast identification and quantification of c-Fos protein using you-only-look-once-v5

et al. 2022

View full text Add to dashboard Cite

In neuroscience, protein activity characterizes neuronal excitability in response to a diverse array of external stimuli and represents the cell state throughout the development of brain diseases. Importantly, it is necessary to characterize the proteins involved in disease progression, nuclear function determination, stimulation method effect, and other aspects. Therefore, the quantification of protein activity is indispensable in neuroscience. Currently, ImageJ software and manual counting are two of the most commonly used methods to quantify proteins. To improve the efficiency of quantitative protein statistics, the you-only-look-once-v5 (YOLOv5) model was proposed. In this study, c-Fos immunofluorescence images data set as an example to verify the efficacy of the system using protein quantitative statistics. The results indicate that YOLOv5 was less time-consuming or obtained higher accuracy than other methods (time: ImageJ software: 80.12 ± 1.67 s, manual counting: 3.41 ± 0.25 s, YOLOv5: 0.0251 ± 0.0003 s, p < 0.0001, n = 83; simple linear regression equation: ImageJ software: Y = 1.013 × X + 0.776, R2 = 0.837; manual counting: Y = 1.0*X + 0, R2 = 1; YOLOv5: Y = 0.9730*X + 0.3821, R2 = 0.933, n = 130). The findings suggest that the YOLOv5 algorithm provides feasible methods for quantitative statistical analysis of proteins and has good potential for application in detecting target proteins in neuroscience.

show abstract

Section: Discussionmentioning

confidence: 99%

Fast identification and quantification of c-Fos protein using you-only-look-once-v5

et al. 2022

View full text Add to dashboard Cite

show abstract

“…However, they cannot deal with large quantities of datasets. The limitation of traditional ML methods is that they cannot learn more complex features and cannot deal with the complex information within images, such as background pavement with different lighting [7,26].…”

Section: Traditional Machine Learning (Ml) Methodsmentioning

confidence: 99%

Crack Detection in Concrete Structures Using Deep Learning

et al. 2022

Self Cite

View full text Add to dashboard Cite

Infrastructure, such as buildings, bridges, pavement, etc., needs to be examined periodically to maintain its reliability and structural health. Visual signs of cracks and depressions indicate stress and wear and tear over time, leading to failure/collapse if these cracks are located at critical locations, such as in load-bearing joints. Manual inspection is carried out by experienced inspectors who require long inspection times and rely on their empirical and subjective knowledge. This lengthy process results in delays that further compromise the infrastructure’s structural integrity. To address this limitation, this study proposes a deep learning (DL)-based autonomous crack detection method using the convolutional neural network (CNN) technique. To improve the CNN classification performance for enhanced pixel segmentation, 40,000 RGB images were processed before training a pretrained VGG16 architecture to create different CNN models. The chosen methods (grayscale, thresholding, and edge detection) have been used in image processing (IP) for crack detection, but not in DL. The study found that the grayscale models (F1 score for 10 epochs: 99.331%, 20 epochs: 99.549%) had a similar performance to the RGB models (F1 score for 10 epochs: 99.432%, 20 epochs: 99.533%), with the performance increasing at a greater rate with more training (grayscale: +2 TP, +11 TN images; RGB: +2 TP, +4 TN images). The thresholding and edge-detection models had reduced performance compared to the RGB models (20-epoch F1 score to RGB: thresholding −0.723%, edge detection −0.402%). This suggests that DL crack detection does not rely on colour. Hence, the model has implications for the automated crack detection of concrete infrastructures and the enhanced reliability of the gathered information.

show abstract

“…Munawar et al [51] wrote an overview of the use of disruptive technologies to move towards automated disaster prediction and forecasting. Tahir et al [52] created a satellite image dataset by using a convolutional neural network-based framework to perform object detection. Qadir et al [53] analyzed various metaheuristic algorithms for pre-disaster assessment and realize the function of UAV path optimization.…”

Section: Assessment and Measurement Of Waterlogging Resiliencementioning

confidence: 99%

Predicting and Improving the Waterlogging Resilience of Urban Communities in China—A Case Study of Nanjing

Cui

Liu

et al. 2022

Buildings

View full text Add to dashboard Cite

In recent years, urban communities in China have been continuously affected by extreme weather and emergencies, among which the rainstorm and waterlogging disasters pose a great threat to infrastructure and personnel safety. Chinese governments issue a series of waterlogging prevention and control policies, but the waterlogging prevention and mitigation of urban communities still needs to be optimized. The concept of “resilience” has unique advantages in the field of community disaster management, and building resilient communities can effectively make up for the limitations of the traditional top-down disaster management. Therefore, this paper focuses on the pre-disaster prevention and control of waterlogging in urban communities of China, following the idea of “concept analysis–influencing factor identification–evaluation indicators selection–impact mechanism analysis–resilience simulation prediction–empirical research–disaster adaptation strategy formulation”. The structural equation model and BP neural network are used by investigating the existing anti-waterlogging capitals of the target community to predict the future waterlogging resilience. Based on this simulation prediction model, and combined with the incentive and restraint mechanisms, suggestions on corrective measures can be put forward before the occurrence of waterlogging.

show abstract

Automatic Target Detection from Satellite Imagery Using Machine Learning

Cited by 40 publications

References 90 publications

Fast identification and quantification of c-Fos protein using you-only-look-once-v5

Fast identification and quantification of c-Fos protein using you-only-look-once-v5

Crack Detection in Concrete Structures Using Deep Learning

Predicting and Improving the Waterlogging Resilience of Urban Communities in China—A Case Study of Nanjing

Contact Info

Product

Resources

About