RoIAlign: Extracting Accurate Region of Interest Features
Region of Interest Align (RoIAlign) is a computer vision operation that extracts small feature maps from regions of interest (RoIs) in object detection and segmentation tasks. This technology accurately aligns the extracted RoI features with the input to improve precision and reduce errors.
RoI Pooling Limitations
RoI Pooling was the previous method used for extracting RoI features. However, it can produce harsh quantization of the extracted regions, resulting in imprecise and inaccurate feature maps. RoI Pooling divides each RoI into a grid of equal-sized bins and extracts the maximum feature value from each bin to make up the feature map. This pooling operation produces a loss of information causing inaccurate final detections.
RoIAlign to the Rescue
RoIAlign improves on the RoI Pooling method by providing precise and accurate RoI features. RoIAlign uses bilinear interpolation to compute the exact values of the input features at four regularly sampled locations in each RoI bin to avoid quantization of RoI boundaries or bins. The result is then aggregated, producing high-quality feature maps.
The RoIAlign operation is made of three main steps:
Step 1: Dividing the RoI
The RoI is divided into an equal grid of bins that are based on the size of the feature map from the convolutions. The size of each of these bins is calculated using the height(H) and width(W) of the feature map, and the height (h) and width (w) of the RoI as shown in the image below:
Step 2: Bilinear Interpolation
The sampled locations on each bin edge are indexed using floating-point numbers to avoid any quantization of the RoI boundaries or bins. Then, bilinear interpolation is used to compute the feature values at each of the sampled locations to improve accuracy as shown in the image below:
Step 3: Aggregation
After computing the feature values at each of the sampled locations, the result is then aggregated, and the maximum or average pooling is used to generate the final RoI feature map as shown in the image below:
Advantages of RoIAlign
The advantages of RoIAlign compared to RoIPool are many:
- RoIAlign produces precise and accurate RoI feature maps.
- RoIAlign eliminates harsh quantization that reduces the accuracy of RoIPool.
- RoIAlign prevents information loss that was present in RoIPool.
- RoIAlign is faster than RoIPool.
Applications of RoIAlign
RoIAlign can be applied to various object detection and segmentation tasks, including:
- Instance segmentation
- Object detection
- Image classification
- Object tracking
- Panoptic segmentation
Limitations of RoIAlign
Despite all its benefits, RoIAlign does have two limitations:
- RoIAlign requires more memory and computing resources than RoI Pooling because it requires calculating feature values at four regularly sampled positions within each bin.
- RoIAlign only works with convolutional neural networks (CNNs) and cannot function on other machine learning architectures.
RoIAlign is a critical computer vision operation that is revolutionizing object detection and segmentation tasks. Its innovative use of bilinear interpolation and precise quantization ensures high-quality feature maps that are used in various applied fields. Its benefits and limitations must be carefully considered when deciding on its use in different applications, but for many users, RoIAlign produces optimal results.
