A detection method for impact point water columns based on improved YOLO X

Wang, Zhi; Shi, Zhangsong; Tong, Jijin; Gong, Wenbin; Wu, Zedong

doi:10.1063/5.0097956

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…the ReLU function in the other three models [40][41][42]. Regarding the Inbox set, AlexNet and ResNet-50 maintained higher performance than the two simpler models.…”

Section: Comparison Of Model Performancementioning

confidence: 97%

“…Among the four models, EfficientNet had the lowest overall performance across all three test data sets in both accuracy and sensitivity (Figure 6a). Even though not necessarily the direct cause, EfficientNet used the sigmoid-based Swish activation function as opposed to the ReLU function in the other three models [40][41][42]. Regarding the Inbox set, AlexNet and ResNet-50 maintained higher performance than the two simpler models.…”

Section: Comparison Of Model Performancementioning

confidence: 99%

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Convolutional Neural Network Classification of Exhaled Aerosol Images for Diagnosis of Obstructive Respiratory Diseases

Talaat

Tan

et al. 2023

JNT

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Aerosols exhaled from the lungs have distinctive patterns that can be linked to the abnormalities of the lungs. Yet, due to their intricate nature, it is highly challenging to analyze and distinguish these aerosol patterns. Small airway diseases pose an even greater challenge, as the disturbance signals tend to be weak. The objective of this study was to evaluate the performance of four convolutional neural network (CNN) models (AlexNet, ResNet-50, MobileNet, and EfficientNet) in detecting and staging airway abnormalities in small airways using exhaled aerosol images. Specifically, the model’s capacity to classify images inside and outside the original design space was assessed. In doing so, multi-level testing on images with decreasing similarities was conducted for each model. A total of 2745 images were generated using physiology-based simulations from normal and obstructed lungs of varying stages. Multiple-round training on datasets with increasing images (and new features) was also conducted to evaluate the benefits of continuous learning. Results show reasonably high classification accuracy on inbox images for models but significantly lower accuracy on outbox images (i.e., outside design space). ResNet-50 was the most robust among the four models for both diagnostic (2-class: normal vs. disease) and staging (3-class) purposes, as well as on both inbox and outbox test datasets. Variation in flow rate was observed to play a more important role in classification decisions than particle size and throat variation. Continuous learning/training with appropriate images could substantially enhance classification accuracy, even with a small number (~100) of new images. This study shows that CNN transfer-learning models could detect small airway remodeling (<1 mm) amidst a variety of variants and that ResNet-50 can be a promising model for the future development of obstructive lung diagnostic systems.

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“…the ReLU function in the other three models [40][41][42]. Regarding the Inbox set, AlexNet and ResNet-50 maintained higher performance than the two simpler models.…”

Section: Comparison Of Model Performancementioning

confidence: 97%

Section: Comparison Of Model Performancementioning

confidence: 99%

Convolutional Neural Network Classification of Exhaled Aerosol Images for Diagnosis of Obstructive Respiratory Diseases

Talaat

Tan

et al. 2023

JNT

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“…Object detection based on deep neural networks has been drawing much research attention in recent years. 1,2 The inference speed of the detection methods plays a critical role in many applications. Object detection methods can be classified into singlestage methods [3][4][5][6][7][8][9][10][11] and two-stage methods.…”

Section: Introductionmentioning

confidence: 99%

Compact Sparse R-CNN: Speeding up sparse R-CNN by reducing iterative detection heads and simplifying feature pyramid network

Xiang

2023

AIP Advances

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Processing a large number of proposals usually takes a significant proportion of inference time in two-stage object detection methods. Sparse regions with CNN features (Sparse R-CNN) was proposed using a small number of learnable proposals to replace the proposals derived from anchors. To decrease the missing rate, Sparse R-CNN uses six iterative detection heads to gradually regress the detection boxes to the corresponding objects, which hence increases the inference time. To reduce the number of iterative heads, we propose the iterative Hungarian assigner that encourages Sparse R-CNN to generate multiple proposals for each object at the inference stage. This decreases the missing rate when the number of iterative heads is small. As a result, Sparse R-CNN using the proposed assigner needs fewer iterative heads but gives higher detection accuracy. Also, we observe that the multi-layer outputs of the feature pyramid network contribute little to Sparse R-CNN and propose using a single-layer output neck to replace it. The single-layer output neck further improves the inference speed of Sparse R-CNN without the cost of detection accuracy. Experimental results show that the proposed iterative Hungarian assigner together with the single-layer output neck improves Sparse R-CNN by 2.5 AP50 on the Microsoft common objects in context (MS-COCO) dataset and improves Sparse R-CNN by 3.0 AP50 on the PASCAL visual object classes (VOC) dataset while decreasing 30% floating point operations (FLOPs).

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