Traffic sign detection and recognition using event camera image reconstruction
This paper presents a method for detection and recognition of traffic signs based on information extracted from an event camera. The solution used a FireNet deep convolutional neural network to reconstruct events into greyscale frames. Two YOLOv4 network models were trained, one based on greyscale images and the other on colour images. The best result was achieved for the model trained on the basis of greyscale images, achieving an efficiency of 87.03%.
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Methods
1x1 Convolution •
Average Pooling •
Batch Normalization •
Bottom-up Path Augmentation •
Convolution •
Cosine Annealing •
CSPDarknet53 •
CutMix •
Darknet-53 •
DropBlock •
FPN •
Global Average Pooling •
Grid Sensitive •
k-Means Clustering •
Label Smoothing •
Logistic Regression •
Max Pooling •
Mish •
PAFPN •
ReLU •
Residual Connection •
Sigmoid Activation •
Softmax •
Softplus •
Spatial Attention Module •
Spatial Pyramid Pooling •
Tanh Activation •
YOLOv3 •
YOLOv4
