Boundary Aware Semantic Segmentation via Loss Function
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current semantic segmentation techniques in computer vision often ignore boundary information, leading to suboptimal performance in identifying and classifying pixels at the boundaries between different classes, which is crucial for accurate scene understanding.
Innovation Solution
A boundary aware loss function and a deep convolutional neural network (DCNN) architecture that incorporates box filtering and attention aware atrous spatial pyramid pooling (ASPP) to emphasize the importance of boundary pixels, reducing information loss and enhancing feature robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional semantic segmentation techniques are used, then the overall segmentation process is simple, but boundary pixels are misclassified leading to poor segmentation accuracy
Solution Approach 1:
The loss function is segmented into two distinct components: a standard cross-entropy loss for general pixel classification and a boundary-aware loss for boundary pixel classification. This segmentation allows the model to treat boundary and non-boundary pixels differently, improving overall segmentation accuracy by addressing the specific challenge of boundary pixel misclassification without requiring complete redesign of the entire loss function.
Solution Approach 2:
The boundary-aware loss function applies local quality by identifying boundary pixels through gradient computation and applying a different loss calculation specifically to these boundary regions. This allows the model to focus computational effort and apply specialized processing where it is most needed (at boundaries) while maintaining standard processing for non-boundary areas, thereby improving accuracy without uniformly increasing complexity across the entire image.
2Reliability
If boundary information is ignored, then the segmentation process is faster and simpler, but boundary regions between classes are misclassified
Solution Approach 1:
The system performs preliminary action by computing gradients and identifying boundary pixels before the main loss calculation. This preliminary identification of boundary regions allows the model to prepare specialized handling for these critical areas, ensuring they receive appropriate attention during training without requiring complex real-time processing during inference, thus maintaining processing speed while improving boundary identification reliability.
Data Source
AI summary
Some aspects of embodiments of the present disclosure relate to using a boundary aware loss function to train a machine learning model for computing semantic segmentation maps from input images. Some aspects of embodiments of the present disclosure relate to deep convolutional neural networks (DCNNs) for computing semantic segmentation maps from input images, where the DCNNs include a box filtering layer configured to box filter input feature maps computed from the input images before supplying box filtered feature maps to an atrous spatial pyramidal pooling (ASPP) layer. Some aspects of embodiments of the present disclosure relate to a selective ASPP layer configured to weight the outputs of an ASPP layer in accordance with attention feature maps.