PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model (2204.02681v1)

Abstract: Real-world applications have high demands for semantic segmentation methods. Although semantic segmentation has made remarkable leap-forwards with deep learning, the performance of real-time methods is not satisfactory. In this work, we propose PP-LiteSeg, a novel lightweight model for the real-time semantic segmentation task. Specifically, we present a Flexible and Lightweight Decoder (FLD) to reduce computation overhead of previous decoder. To strengthen feature representations, we propose a Unified Attention Fusion Module (UAFM), which takes advantage of spatial and channel attention to produce a weight and then fuses the input features with the weight. Moreover, a Simple Pyramid Pooling Module (SPPM) is proposed to aggregate global context with low computation cost. Extensive evaluations demonstrate that PP-LiteSeg achieves a superior trade-off between accuracy and speed compared to other methods. On the Cityscapes test set, PP-LiteSeg achieves 72.0% mIoU/273.6 FPS and 77.5% mIoU/102.6 FPS on NVIDIA GTX 1080Ti. Source code and models are available at PaddleSeg: https://github.com/PaddlePaddle/PaddleSeg.

• The paper introduces PP-LiteSeg, a novel model that optimizes real-time semantic segmentation with a lightweight, multi-component architecture.
• It leverages a flexible decoder, unified attention fusion module, and simple pyramid pooling to balance computational efficiency and segmentation accuracy.
• Empirical results on Cityscapes and CamVid show significant improvements, with performance up to 77.5% mIoU at over 100 FPS.

Overview of "PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model"

Semantic segmentation is an essential task within computer vision, serving applications like autonomous driving and medical imaging. Despite considerable advancements driven by deep learning, the challenge of executing segmentation in real-time with high accuracy persists. The paper introduces PP-LiteSeg, a novel approach aimed at addressing this challenge by enhancing the trade-off between computational efficiency and segmentation accuracy.

Methodological Innovations

PP-LiteSeg employs a conventional encoder-decoder architecture, enriched with three key components:

1. Flexible and Lightweight Decoder (FLD): The FLD reduces computational redundancy by gradually decreasing feature channels from deep to shallow layers. This design optimizes the balance of computational loads between the encoder and decoder, maintaining efficiency without compromising precision.
2. Unified Attention Fusion Module (UAFM): UAFM integrates spatial and channel attention mechanisms to enhance feature fusion effectively. This attention-based fusion facilitates stronger feature representations, which are critical for improving segmentation accuracy.
3. Simple Pyramid Pooling Module (SPPM): SPPM aggregates global contextual information with minimal computational overhead. By simplifying operations, such as reducing intermediate channels and replacing concatenation with addition, SPPM maintains low latency—crucial for real-time applications.

Empirical Evaluation

PP-LiteSeg's capabilities are demonstrated on benchmark datasets Cityscapes and CamVid. On the Cityscapes test set, PP-LiteSeg-T achieved 72.0% mIoU at 273.6 FPS, whereas PP-LiteSeg-B reached 77.5% mIoU at 102.6 FPS, showcasing an impressive balance of accuracy and speed. Comparatively, the model shows improved performance over existing methods such as BiSeNet and STDC, particularly in maintaining high inference rates.

Implications and Future Work

The development of PP-LiteSeg presents a promising step towards more efficient real-time semantic segmentation. Its architecture, characterized by lightweight modules and efficient feature fusion, serves as a blueprint for future research in real-time applications, potentially extending to interactive segmentation and matting. This work opens opportunities for further exploration into optimizing attention mechanisms and pooling methods within segmentation networks.

Conclusion

PP-LiteSeg offers a significant contribution to the field of real-time semantic segmentation by optimizing the balance between computational efficiency and accuracy. While PP-LiteSeg demonstrates state-of-the-art performance compared to existing models, the exploration of its components may inspire future advancements in real-time computer vision tasks. The emphasis on attention-driven fusion and efficient computation provides a foundation for ongoing research and application diversification.