Introduction to R-FCN
R-FCN or Region-based Fully Convolutional Networks is a type of region-based object detector. Unlike previous object detectors where a costly per-region subnetwork is applied hundreds of times, R-FCN is a fully convolutional network, with almost all computation shared on the entire image.
How R-FCN Works
R-FCN achieves this by utilising position-sensitive score maps. These score maps are used to address a dilemma between translation-invariance in image classification and translation-variance in object detection. In traditional object detection methods, such as Fast R-CNN, a classification layer is added after each region proposal. This classification layer is a fully connected layer that produces an output vector for each proposal. The output vector contains a score for each class and bounding box proposals. In R-FCN, the convolution layer which is responsible for learning the feature maps, is shared for all proposals in the input image. The class scores are then calculated on position-sensitive feature maps. These score maps efficiently encode geometric structure, while preserving translation invariance in object detection. Position sensitive score maps are used to encode as structural spatial information to the feature map. This encoding breaks down the convolutional output of an image into bins. Each bin is assigned a class score. By doing this, R-FCN can perform accurate object detection by allowing each object instance to vote in the class score of its corresponding bin.
Advantages of R-FCN
R-FCN has several advantages over traditional object detection methods. Firstly, R-FCN uses a shared convolutional network that avoids the costly per-region subnetwork that other methods use. This allows R-FCN to save computation time and memory consumption. Another advantage of R-FCN is that position-sensitive score maps are used to encode structural spatial information to the feature map, which improves localization accuracy. This method also preserves translation invariance in object detection. R-FCN also has the added benefit of being fully convolutional, which means it can be applied to images of different sizes without the need for cropping or scaling. This allows R-FCN to process images in their entirety and improve overall object detection accuracy.
Applications of R-FCN
R-FCN has a wide range of applications in various fields. It can be used for object detection in autonomous vehicles, where it can identify objects such as pedestrians, traffic signals, and other vehicles. R-FCN can also be used in healthcare applications, where it can be used to identify cancerous cells in medical imagery. R-FCN can also be used in environmental monitoring, where it can identify and track different animal species. It can also be used as a tool in crime prevention, where it can be used to track and detect suspicious activity in public areas.In summary, R-FCN is a powerful region-based object detector that utilises position-sensitive score maps to effectively address the dilemma between translation-invariance in image classification and translation-variance in object detection. R-FCN has numerous advantages over traditional object detection methods, including computational savings, increased detection accuracy, and the ability to process images of varying sizes. It has wide-ranging applications in fields such as healthcare, environmental monitoring, and crime prevention. With the advancement of technology, R-FCN is set to become an increasingly important tool for object detection in a range of applications.
