ConvNets for Counting: Object Detection of Transient Phenomena in Steelpan Drums

1 Feb 2021 Scott H. Hawley Andrew C. Morrison

We train an object detector built from convolutional neural networks to count interference fringes in elliptical antinode regions visible in frames of high-speed video recordings of transient oscillations in Caribbean steelpan drums illuminated by electronic speckle pattern interferometry (ESPI). The annotations provided by our model, "SPNet" are intended to contribute to the understanding of time-dependent behavior in such drums by tracking the development of sympathetic vibration modes... (read more)

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Methods used in the Paper


METHOD TYPE
Average Pooling
Pooling Operations
Pointwise Convolution
Convolutions
Depthwise Convolution
Convolutions
Batch Normalization
Normalization
Cutout
Image Data Augmentation
Dropout
Regularization
1cycle
Learning Rate Schedules
Weight Decay
Regularization
Residual Connection
Skip Connections
1x1 Convolution
Convolutions
Softmax
Output Functions
Dense Connections
Feedforward Networks
Global Average Pooling
Pooling Operations
Max Pooling
Pooling Operations
Depthwise Separable Convolution
Convolutions
Xception
Convolutional Neural Networks
YOLOv2
Object Detection Models
GAN Least Squares Loss
Loss Functions
Cycle Consistency Loss
Loss Functions
ReLU
Activation Functions
Sigmoid Activation
Activation Functions
Leaky ReLU
Activation Functions
Residual Block
Skip Connection Blocks
Convolution
Convolutions
Instance Normalization
Normalization
PatchGAN
Discriminators
CycleGAN
Generative Models