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crlnet
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Pytorch implementation of our paper "CLRNet: Cross Layer Refinement Network for Lane Detection" (CVPR2022 Acceptance). - Turoad/CLRNet
The natural environment presents a multitude of scenes with diverse content, posing challenges for satisfactory segmentation results using existing segmentation networks. In response, we propose a Cascaded Resolution Learning Network (CRLNet) to enhance segmentation performance through global textual embedding and multi-resolution feature learning. The CRLNet constructs a multi-path ...
Accurate peach detection is essential for automated agronomic management, such as mechanical peach harvesting. However, ubiquitous occlusion makes identifying peaches from complex backgrounds extremely challenging. In addition, it is difficult to capture fine-grained peach features from a single RGB image, which can suffer from light and noise in scenarios with dense small target clusters and ...
CRLNet Existing network designs fail to address the issue of significant information loss during the process of feature extraction in a single layer. The feature extraction network of YOLOv9 is equipped with ELAN blocks, which provide a comprehensive stream of gradient information, enabling the network to perform feature extraction on the input ...
Abstract Lane is critical in the vision navigation system of the intelligent vehicle. Naturally, lane is a trafic sign with high-level semantics, whereas it owns the specific local pattern which needs detailed low-level features to localize accurately. Using different feature levels is of great im-portance for accurate lane detection, but it is still under-explored. In this work, we present ...
TI - CRLNet: Cascaded Resolution Learning Network for Natural Scenes Segmentation T2 - IEEE Intelligent Systems AU - Li, Wei AU - Tian, Shishun AU - Hua, Guoguang AU - Liao, Muxin AU - Zhang, Yuhang AU - Zou, Wenbin PY - 2025 DA - 2025/03/07 PB - Institute of Electrical and Electronics Engineers (IEEE) SP - 1-8 SN - 1541-1672 SN - 1941-1294 ER ...
Request PDF | On Jan 1, 2024, Wei Li and others published Crlnet: Cascaded Resolution Learning Network for Unstructured Semantic Segmentation in Natural Scenes | Find, read and cite all the ...
Experimental results demonstrate that CRLNet outperforms state-of-the-art methods on the Huanghe River and Yancheng coastal wetland datasets. Notably, CRLNet is a lightweight framework with only 1 55 of the parameter count of CGGLNet, making it computationally efficient while maintaining high classification accuracy.
Lane is critical in the vision navigation system of the intelligent vehicle. Naturally, lane is a traffic sign with high-level semantics, whereas it owns the specific local pattern which needs detailed low-level features to localize accurately. Using different feature levels is of great importance for accurate lane detection, but it is still under-explored. In this work, we present Cross Layer ...
In modern electronic countermeasures, accurately identifying the modulation patterns of enemy radar signals is of vital importance for electronic reconnaissance and intelligence acquisition. A hybrid network model named CRLNet based on the convolutional neural network (CNN) and residual modules is proposed, which innovatively introduces the rotary position embedding (RoPE) and the linear ...