GPNet: Gated pyramid network for semantic segmentation

Zhang, Yu; Sun, Xin; Dong, Junyu; Chen, Changrui; Lv, Qingxuan

doi:10.1016/j.patcog.2021.107940

Cited by 43 publications

(8 citation statements)

References 19 publications

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“…Feature hierarchy ideas can be invested in the field of semantic segmentation by combining the low-level features with high-level features in a top-down module and skip connections. This module upsamples spatially coarser feature maps from higher pyramid levels and then tunes them with features from the bottom-up module [ 29 , 30 ]. Each skip connection merges between two feature maps of the same dimensions so that one comes from the bottom-up path and the other one comes from the top-down path.…”

Section: Preliminaries and Discussionmentioning

confidence: 99%

Intelligent Semantic Segmentation for Self-Driving Vehicles Using Deep Learning

Sellat

Bisoy

Priyadarshini

et al. 2022

Computational Intelligence and Neuroscience

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Understanding the situation is a critical component of any self-driving system. Accurate real-time visual signal processing to create pixelwise classed pictures, also known as semantic segmentation, is critical for scenario comprehension and subsequent acceptance of this new technology. Due to the intricate interaction between pixels in each frame of the received camera data, such efficiency in terms of processing time and accuracy could not be achieved prior to recent advances in deep learning algorithms. We present an effective approach for semantic segmentation for self-driving automobiles in this study. We combine deep learning architectures like convolutional neural networks and autoencoders, as well as cutting-edge approaches like feature pyramid networks and bottleneck residual blocks, to develop our model. The CamVid dataset, which has undergone considerable data augmentation, is utilised to train and test our model. To validate the suggested model, we compare the acquired findings to various baseline models reported in the literature.

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Section: Preliminaries and Discussionmentioning

confidence: 99%

Intelligent Semantic Segmentation for Self-Driving Vehicles Using Deep Learning

Sellat

Bisoy

Priyadarshini

et al. 2022

Computational Intelligence and Neuroscience

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“…Zhang et al [33] devised an innovative network known as GPNet, which effectively filters multi-scale data through a gated and paired approach, enabling comprehensive data aggregation. The Gated Pyramid Module (GPM) is custom-designed to integrate low-level and highlevel features, yielding receptive fields that are both dense and expansive.…”

Section: Plos Onementioning

confidence: 99%

Semantic segmentation of urban environments: Leveraging U-Net deep learning model for cityscape image analysis

Arulananth,

Kuppusamy,

Ayyasamy

et al. 2024

PLoS ONE

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Semantic segmentation of cityscapes via deep learning is an essential and game-changing research topic that offers a more nuanced comprehension of urban landscapes. Deep learning techniques tackle urban complexity and diversity, which unlocks a broad range of applications. These include urban planning, transportation management, autonomous driving, and smart city efforts. Through rich context and insights, semantic segmentation helps decision-makers and stakeholders make educated decisions for sustainable and effective urban development. This study investigates an in-depth exploration of cityscape image segmentation using the U-Net deep learning model. The proposed U-Net architecture comprises an encoder and decoder structure. The encoder uses convolutional layers and down sampling to extract hierarchical information from input images. Each down sample step reduces spatial dimensions, and increases feature depth, aiding context acquisition. Batch normalization and dropout layers stabilize models and prevent overfitting during encoding. The decoder reconstructs higher-resolution feature maps using "UpSampling2D" layers. Through extensive experimentation and evaluation of the Cityscapes dataset, this study demonstrates the effectiveness of the U-Net model in achieving state-of-the-art results in image segmentation. The results clearly shown that, the proposed model has high accuracy, mean IOU and mean DICE compared to existing models.

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“…The pyramid structure is a hierarchical structure that can preserve multi-scale features including both the local details and the global arrangement information. For pixel-wise prediction tasks such as semantic segmation [ 46 , 47 , 48 , 49 , 50 , 51 ], object detection [ 52 , 53 , 54 , 55 , 56 , 57 ] and depth map estimation [ 20 , 21 , 58 , 59 , 60 , 61 ], fusing features in different types and scales can be very helpful for DNN models to learn more fruitful information and then gain better performance.…”

Section: Related Workmentioning

confidence: 99%

“…Zhang et al. [ 51 ] proposed a gated pyramid network (GPNet), in which the features in backbone pyramid are sent to a gated pyramid module, then the output is sent to three cross-layer attention modules for further prediction.…”

Section: Related Workmentioning

confidence: 99%

DCPNet: A Densely Connected Pyramid Network for Monocular Depth Estimation

Lai

Tian

et al. 2021

Sensors

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Pyramid architecture is a useful strategy to fuse multi-scale features in deep monocular depth estimation approaches. However, most pyramid networks fuse features only within the adjacent stages in a pyramid structure. To take full advantage of the pyramid structure, inspired by the success of DenseNet, this paper presents DCPNet, a densely connected pyramid network that fuses multi-scale features from multiple stages of the pyramid structure. DCPNet not only performs feature fusion between the adjacent stages, but also non-adjacent stages. To fuse these features, we design a simple and effective dense connection module (DCM). In addition, we offer a new consideration of the common upscale operation in our approach. We believe DCPNet offers a more efficient way to fuse features from multiple scales in a pyramid-like network. We perform extensive experiments using both outdoor and indoor benchmark datasets (i.e., the KITTI and the NYU Depth V2 datasets) and DCPNet achieves the state-of-the-art results.

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GPNet: Gated pyramid network for semantic segmentation

Cited by 43 publications

References 19 publications

Intelligent Semantic Segmentation for Self-Driving Vehicles Using Deep Learning

Intelligent Semantic Segmentation for Self-Driving Vehicles Using Deep Learning

Semantic segmentation of urban environments: Leveraging U-Net deep learning model for cityscape image analysis

DCPNet: A Densely Connected Pyramid Network for Monocular Depth Estimation

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