LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation (1905.02423v3)

Abstract: The extensive computational burden limits the usage of CNNs in mobile devices for dense estimation tasks. In this paper, we present a lightweight network to address this problem,namely LEDNet, which employs an asymmetric encoder-decoder architecture for the task of real-time semantic segmentation.More specifically, the encoder adopts a ResNet as backbone network, where two new operations, channel split and shuffle, are utilized in each residual block to greatly reduce computation cost while maintaining higher segmentation accuracy. On the other hand, an attention pyramid network (APN) is employed in the decoder to further lighten the entire network complexity. Our model has less than 1M parameters,and is able to run at over 71 FPS in a single GTX 1080Ti GPU. The comprehensive experiments demonstrate that our approach achieves state-of-the-art results in terms of speed and accuracy trade-off on CityScapes dataset.

• The paper introduces a novel lightweight encoder-decoder architecture with channel split and shuffle, enabling efficient real-time segmentation.
• The paper demonstrates that the Attention Pyramid Network in the decoder refines feature extraction, achieving over 70% class IoU on the CityScapes dataset.
• The paper’s design reduces model parameters to under 1 million, making it ideal for mobile and embedded applications.

LEDNet: A Lightweight Network for Real-Time Semantic Segmentation

In the domain of computer vision, real-time semantic segmentation remains a crucial task, particularly for applications like robotics, augmented reality, and autonomous vehicles. These applications demand a balance between computational efficiency and segmentation accuracy. The paper "LEDNet: a lightweight encoder-decoder network for real-time semantic segmentation" introduces a novel approach that addresses this balance efficiently.

The authors propose LEDNet, a convolutional neural network (CNN) featuring an asymmetric encoder-decoder architecture designed for real-time applications on computationally limited devices. LEDNet distinguishes itself through several key innovations in its architectural design, notably with its lightweight model, which costs less than 1 million parameters and achieves over 71 FPS on a GTX 1080Ti GPU.

Key Contributions

1. Asymmetric Encoder-Decoder Architecture: LEDNet incorporates an encoder leveraging a modified ResNet backbone, where the introduction of channel split and shuffle operations within residual blocks significantly reduces computational overhead. This redesign maintains a high level of accuracy while optimizing resource usage.
2. Attention Pyramid Network (APN) in the Decoder: The decoder integrates an attention mechanism through an APN to refine semantic feature extraction further. This component differs from traditional dilated convolutions by using a feature pyramid that effectively balances receptive field size and network complexity. This strategy allows the network to achieve finer segmentation details across multiple scales.
3. Efficiency and Performance: The authors conducted extensive experiments showcasing that LEDNet achieves a superior balance between speed and accuracy on the CityScapes dataset, achieving a class mean IoU of 70.6% and category mean IoU of 87.1%. Compared to other state-of-the-art lightweight models, LEDNet demonstrates notable improvements in both segmentation accuracy and real-time performance.

Implications and Future Scope

The implications of LEDNet are significant for real-world applications that operate under constrained computational resources. By reducing the parameter count and inference time without compromising accuracy, LEDNet is poised to influence the design of future models in low-power environments like mobile or embedded systems.

From a theoretical perspective, LEDNet’s utilization of split and shuffle operations in conjunction with attention mechanisms highlights a growing trend in CNN design, focusing on efficient data path designs to improve feature representation power. The idea of feature reuse through shuffling operations suggests broader applications in architecture design where parameter efficiency is prioritized.

Looking forward, enhancements could involve further refining the decoder structure, possibly by introducing depthwise separable convolutions in the APN to reduce complexity even more. Additionally, exploring the adaptation of LEDNet for other dense prediction tasks beyond semantic segmentation could provide interesting directions for extending its utility.

In summary, the LEDNet paper presents a compelling approach to real-time semantic segmentation by exploiting novel architectural choices, balancing performance metrics, and accommodating the practical constraints inherent to mobile and embedded platforms. As such, it sets a benchmark for subsequent efforts aiming to achieve efficient and accurate semantic segmentation.