DILane: Dynamic Instance-Aware Network for Lane Detection

Cheng, Zhengyun; Zhang, Guanwen; Wang, Changhao; Zhou, Wei

doi:10.1007/978-3-031-26284-5_8

Cited by 1 publication

(2 citation statements)

References 33 publications

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“…This limits their reliability in ensuring the safe navigation of autonomous driving vehicles through container terminals. (4) Anchor-based methods are primarily categorized into two types: line anchor-based detection [16][17][18][19] and row anchor-based detection [20][21][22]. Line anchor-based methods utilize the prior shape of lane lines and pre-set anchor points at the image borders for classification and regression, offering high efficiency.…”

Section: Lane Detection Methodsmentioning

confidence: 99%

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PortLaneNet: A Scene-Aware Model for Robust Lane Detection in Container Terminal Environments

Ye,

Kang,

Zhou

et al. 2024

WEVJ

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In this paper, we introduce PortLaneNet, an optimized lane detection model specifically designed for the unique challenges of enclosed container terminal environments. Unlike conventional lane detection scenarios, this model addresses complexities such as intricate ground markings, tire crane lane lines, and various types of regional lines that significantly complicate detection tasks. Our approach includes the novel Scene Prior Perception Module, which leverages pre-training to provide essential prior information for more accurate lane detection. This module capitalizes on the enclosed nature of container terminals, where images from similar area scenes offer effective prior knowledge to enhance detection accuracy. Additionally, our model significantly improves understanding by integrating both high- and low-level image features through attention mechanisms, focusing on the critical components of lane detection. Through rigorous experimentation, PortLaneNet has demonstrated superior performance in port environments, outperforming traditional lane detection methods. The results confirm the effectiveness and superiority of our model in addressing the complex challenges of lane detection in such specific settings. Our work provides a valuable reference for solving lane detection issues in specialized environments and proposes new ideas and directions for future research.

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Section: Lane Detection Methodsmentioning

confidence: 99%

“…Notable methods in this category include Line-CNN [16], LaneATT [17], and CLRNet [18]. A dynamic head and selfattention module were designed in DILane [19], utilizing cross-layer features and global context information. A cross-layer feature optimization strategy was proposed in CLRNet.…”

Section: Lane Detection Methodsmentioning

confidence: 99%