CABiNet: A Context Aggregation Network for Efficient Semantic Segmentation
As the demand for autonomous systems continues to increase, there is a greater need for efficient, real-time visual scene understanding. To address this need, researchers have proposed the Context Aggregation Network (CABiNet), a dual-branch convolutional neural network designed for pixelwise semantic segmentation.
Compared to other state-of-the-art methods, CABiNet offers significantly lower computational costs without sacrificing accuracy. This is achieved through the use of a high-resolution branch that allows for effective spatial detailing, as well as a context branch that incorporates lightweight global aggregation and local distribution blocks. These features enable CABiNet to capture both long-range and local contextual dependencies necessary for accurate semantic segmentation, while keeping computational overheads low.
Dual-Branch Architecture for High-Speed Semantic Segmentation
CABiNet builds upon existing dual-branch architectures for high-speed semantic segmentation. The high-resolution branch is responsible for capturing detailed spatial information, while the context branch aggregates information from surrounding context to provide a more comprehensive understanding of the scene.
To achieve this, the context branch includes global and local blocks that allow for effective information aggregation. The global block uses a lightweight version of global average pooling, while the local block uses dilated convolutions to capture local dependencies across different scales. Together, these blocks enable CABiNet to accurately capture both local and global context, while keeping computational costs low.
Efficient Semantic Segmentation with State-of-the-Art Results
CABiNet has been evaluated on two semantic segmentation datasets: Cityscapes and UAVid. For the Cityscapes test set, CABiNet achieved state-of-the-art results with a mean intersection over union (mIOU) score of 75.9%, at 76 frames per second (FPS) on an NVIDIA RTX 2080Ti and 8 FPS on a Jetson Xavier NX. Similarly, on the UAVid dataset, CABiNet achieved an mIOU score of 63.5% with high execution speed (15 FPS).
In summary, CABiNet offers an efficient, high-speed solution for pixelwise semantic segmentation that maintains state-of-the-art accuracy. With its dual-branch architecture and lightweight global and local aggregation blocks, CABiNet is poised to meet the increasing demand for autonomous systems that require real-time visual scene understanding.
