HDNet: Hybrid Distance Network for semantic segmentation

Li, Chunpeng; Kang, Xuejing; Zhu, Lei; Ye, Lizhu; Feng, Panhe; Ming, Anlong

doi:10.1016/j.neucom.2021.03.044

Cited by 5 publications

(1 citation statement)

References 11 publications

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“…Zhong et al [22] used the class level mask as an auxiliary loss function. Li et al [44] introduced a hybrid distance relation to capture contexts in a feature map by a combination of positional distance. The segmentation error was analysed in Ref.…”

Section: Boundary Supervisionmentioning

confidence: 99%

Refinement Co‐supervision network for real‐time semantic segmentation

Dong

Zhao

Zheng

et al. 2023

IET Computer Vision

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Semantic segmentation is a fundamental technology for autonomous driving. It has a high demand for inference speed and accuracy. However, a good trade‐off between accuracy and latency is yet not present in existing semantic segmentation approaches. Due to the limitation of speed, the authors cannot increase the number of network layers without limit and cannot design modules like in the networks without real‐time. It is a challenging problem how to design a model with good performance under limited resources. To alleviate these issues, in this study, the authors propose a refinement co‐supervision network (RCNet), which is real‐time on a high‐resolution image (1024×2048). The authors first construct the context refinement module, which can provide low computation cost way for obtaining the large receptive field and context information. Furthermore, a boundary co‐supervision mechanism is proposed. It strengthens the optimisation of easily neglected boundaries and small targets. Experimental results reveal that the proposed RCNet outperforms seven representative semantic segmentation methods.

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Section: Boundary Supervisionmentioning

confidence: 99%

Refinement Co‐supervision network for real‐time semantic segmentation

Dong

Zhao

Zheng

et al. 2023

IET Computer Vision

View full text Add to dashboard Cite

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FuseNet: 3D Object Detection Network with Fused Information for Lidar Point Clouds

Liu

Tian

Wang

et al. 2022

Neural Process Lett

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In order to deal with the sparse and unstructured raw point clouds, LiDAR based 3D object detection research mostly focuses on designing dedicated local point aggregators for fine-grained geometrical modeling. In this paper, we revisit the local point aggregators from the perspective of allocating computational resources. We find that the simplest pillar based models perform surprisingly well considering both accuracy and latency. Additionally, we show that minimal adaptions from the success of 2D object detection, such as enlarging receptive field, significantly boost the performance. Extensive experiments reveal that our pillar based networks with modernized designs in terms of architecture and training render the state-of-the-art performance on the two popular benchmarks: Waymo Open Dataset and nuScenes. Our results challenge the common intuition that the detailed geometry modeling is essential to achieve high performance for 3D object detection.

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