π€³SSD and YOLO
Single Shot Detectors and You Only Look Once
π You Only Look Once
π₯ The approach involves a single neural network trained end to end
It takes an image as input and predicts bounding boxes and class labels for each bounding box directly.
π The technique offers lower predictive accuracy (e.g. more localization errors) Compared with region based models
β YOLO divides the input image into an SΓS grid. Each grid cell predicts only one object
π·ββοΈ Long Story Short: The system divides the input image into an S Γ S grid. If the center of an object falls into a grid cell, that grid cell is responsible for detecting that object.
π Advantages
π Speed
π€ΈββοΈ Feasible for real time applications
π Disadvantages
π Poor performance on small-sized objects
It tends to give imprecise object locations.
TODO: Compare versions of YOLO
π€ΈββοΈ SSD
π₯ Predicts objects in images using a single deep neural network.
π€ The network generates scores for the presence of each object category using small convolutional filters applied to feature maps.
β This approach uses a feed-forward CNN that produces a collection of bounding boxes and scores for the presence of certain objects.
β In this model, each feature map cell is linked to a set of default bounding boxes
π©βπ« Details
πΌοΈ After going through a certain of convolutions for feature extraction, we obtain a feature layer of size mΓn (number of locations) with p channels, such as 8Γ8 or 4Γ4 above.
And a 3Γ3 conv is applied on this mΓnΓp feature layer.
π For each location, we got k bounding boxes. These k bounding boxes have different sizes and aspect ratios.
The concept is, maybe a vertical rectangle is more fit for human, and a horizontal rectangle is more fit for car.
π« For each of the bounding boxes, we will compute
c
class scores and 4 offsets relative to the original default bounding box shape.
π€ Long Story Short
The SSD object detection algorithm is composed of 2 parts:
Extract feature maps
Apply convolution filters to detect objects.
π΅οΈββοΈ Evaluation
Better accuracy compared to YOLO
Better speed compared to Region based algorithms
π Visualization
π« SSD vs YOLO
π§ References
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