vision-based tasks of Unmanned Aerial Vehicles (UAVs) attracted significant research attention. Nowadays, testing a complex vision-based system using real experiments is expensive and time-consuming. This paper focuse...
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vision-based tasks of Unmanned Aerial Vehicles (UAVs) attracted significant research attention. Nowadays, testing a complex vision-based system using real experiments is expensive and time-consuming. This paper focuses on designing and implementing an integrated virtual simulation and test system for vision-based algorithm verification in visual perception, communication transmission, and closed-loop control of UAVs. First, a unified modular modeling process is proposed to construct the test system, which consists of a virtual environment subsystem, a vision algorithm subsystem, and a corresponding control subsystem. Although the vision algorithm and control subsystems are the same real experiments and virtual simulation, the virtual environment subsystem focuses on ensuring simulation credibility as the only difference between real and virtual experimentation. Then, some essential components are verified, including the camera model, image process, and communication of the visual sensor network. Finally, two case studies are performed to validate the efficiency and easy deployment, illustrating it as a promising platform for verifying and validating advanced guidance, navigation, and control algorithms.
Concrete structures are prone to developing cracks, which can have a negative impact on their overall performance and longevity. It is essential to promptly identify and repair these cracks in order to ensure the stru...
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Concrete structures are prone to developing cracks, which can have a negative impact on their overall performance and longevity. It is essential to promptly identify and repair these cracks in order to ensure the structural integrity of the building. The present research concentrates on the development of crack diagnosis algorithmsbased on vision using an optimized version of Deep Neural Network (DNN). The DNN model employed in the current study is the deep belief network (DBN), while the optimization technique is based on a newly designed variant of the Ideal Gas Molecular Movement (MIGMM). By combining these two components, a highly effective crack detection system is created, capable of achieving higher classification rates. To train the DNN model, an image dataset comprising two classes, namely "no-cracks" and "cracks", has been utilized. The MIGMM has been applied to the DBN model, involving fine-tuning the network architecture's weights, substituting the categorization layer with two classes of output (cracks and no-cracks), and augmenting the picture dataset using stochastic angles of rotation. The proposed DBN/MIGMM model achieves exceptional performance, with an accuracy of 90.189%, specificity of 94.502%, precision of 94.586%, recall of 94.529%, and an F1-score of 88.093%, outperforming state-of-the-art methods such as Fully Convolutional Networks (FCN), You Only Look Once (YOLO), CrackSegNet, Convolutional Neural Networks (CNN), and Convolutional Encoder-Decoder Networks (CedNet). The present outcomes prepare a comprehensive superior assessment of the proposed model's effectiveness in accurately detecting and classifying cracks.
Non-nutritive sucking (NNS), which refers to the act of sucking on a pacifier, finger, or similar object without nutrient intake, plays a crucial role in assessing healthy early development. In the case of preterm inf...
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Non-nutritive sucking (NNS), which refers to the act of sucking on a pacifier, finger, or similar object without nutrient intake, plays a crucial role in assessing healthy early development. In the case of preterm infants, NNS behavior is a key component in determining their readiness for feeding. In older infants, the characteristics of NNS behavior offer valuable insights into neural and motor development. Additionally, NNS activity has been proposed as a potential safeguard against sudden infant death syndrome (SIDS). However, the clinical application of NNS assessment is currently hindered by labor-intensive and subjective finger-in-mouth evaluations. Consequently, researchers often resort to expensive pressure transducers for objective NNS signal measurement. To enhance the accessibility and reliability of NNS signal monitoring for both clinicians and researchers, we introduce a vision-based algorithm designed for non-contact detection of NNS activity using baby monitor footage in natural settings. Our approach involves a comprehensive exploration of optical flow and temporal convolutional networks, enabling the detection and amplification of subtle infant-sucking signals. We successfully classify short video clips of uniform length into NNS and non-NNS periods. Furthermore, we investigate manual and learning-based techniques to piece together local classification results, facilitating the segmentation of longer mixed-activity videos into NNS and non-NNS segments of varying duration. Our research introduces two novel datasets of annotated infant videos, including one sourced from our clinical study featuring 18 infant subjects and 183 h of overnight baby monitor footage. Additionally, we incorporate a second, shorter dataset obtained from publicly available YouTube videos. Our NNS action recognition algorithm achieves an impressive 95.8% accuracy in binary classification, based on 960 2.5-s balanced NNS versus nonNNS clips from our clinical dataset. We also pre
Behaviours of dairy cows reflect their health and emotions. Behavioural analysis by video surveillance is an accepted technique for helping cow-keepers to spot their cows' health problems. To perform a behavioural...
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Behaviours of dairy cows reflect their health and emotions. Behavioural analysis by video surveillance is an accepted technique for helping cow-keepers to spot their cows' health problems. To perform a behavioural analysis, the presence and location of the cows need to be detected first. In this study, we used feature point matching method and foreground detection method to detect them. Two experiments were conducted in a dairy farm to detect cows in video frames recorded by a video camera installed over the top of a free-stall barn. A total of 800 frames of recorded cows' activities were captured. True and false positive and negative results were statistically confirmed by t test. We found that the accuracies of the feature point matching and foreground detection methods were 38.55 and 75.95 %, respectively;hence, for our setup, the foreground detection was a better method.
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