Foggy Lane Dataset Synthesized from Monocular Images for Lane Detection Algorithms

Nie, Xiangyu; Xu, Zhejun; Zhang, Wei; Dong, Xue; Liu, Ning; Chen, Yong

doi:10.3390/s22145210

Cited by 7 publications

(3 citation statements)

References 51 publications

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“…Similar approaches have been used to augment existing datasets for scenes such as rainy [ 13 ] days and nighttime [ 14 ]. For example, the literature [ 15 ] discusses the use of synthetic images for lane line detection, while previous studies have investigated synthetic fog images for automated driving, including FRIDA [ 16 ], FRIDA2 [ 17 ], Foggy Cityscape [ 18 ], and Multifog KITTI [ 19 ]; few studies have quantitatively evaluated artificially generated fog image enhancement datasets to determine their fidelity. Tarel et al constructed digital image-based synthetic outdoor fog datasets, namely FRIDA (Foggy Road Image Database) and FRIDA2, using

software, but these datasets contain a limited number of images with low resolution and poor visual quality and are therefore insufficient to support the training of target detection models with higher accuracy and on a larger scale, especially in fog scenes.…”

Section: Introductionmentioning

confidence: 99%

A Foggy Weather Simulation Algorithm for Traffic Image Synthesis Based on Monocular Depth Estimation

Tang,

Zhao,

Qiu

2024

Sensors

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This study addresses the ongoing challenge for learning-based methods to achieve accurate object detection in foggy conditions. In response to the scarcity of foggy traffic image datasets, we propose a foggy weather simulation algorithm based on monocular depth estimation. The algorithm involves a multi-step process: a self-supervised monocular depth estimation network generates a relative depth map and then applies dense geometric constraints for scale recovery to derive an absolute depth map. Subsequently, the visibility of the simulated image is defined to generate a transmittance map. The dark channel map is then used to distinguish sky regions and estimate atmospheric light values. Finally, the atmospheric scattering model is used to generate fog simulation images under specified visibility conditions. Experimental results show that more than 90% of fog images have AuthESI values of less than 2, which indicates that their non-structural similarity (NSS) characteristics are very close to those of natural fog. The proposed fog simulation method is able to convert clear images in natural environments, providing a solution to the problem of lack of foggy image datasets and incomplete visibility data.

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Section: Introductionmentioning

confidence: 99%

A Foggy Weather Simulation Algorithm for Traffic Image Synthesis Based on Monocular Depth Estimation

Tang,

Zhao,

Qiu

2024

Sensors

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“…Additionally, efforts have been made to generate datasets with complex weather conditions, aiming to increase the diversity of available data. For instance, [14] and [15] successfully synthesized foggy and blurry images from data originally collected under clear daytime conditions. Similar attempts have been made to transform daytime images into nighttime representations [16].…”

Section: Introductionmentioning

confidence: 99%

A Robust Lane Detection Algorithm Adaptable to Challenging Weather Conditions

Sang,

Norris

2024

IEEE Access

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Driver assistance systems and autonomous vehicle navigation have become important topics in vehicular technology. Among all of the functions, lane detection is one of the most important. A variety of approaches have been proposed in this field. While learning-based methods achieve impressive accuracy in detecting complex lane markings under clear daylight conditions, adapting these models to diverse weather conditions remains a challenge. On the other hand, geometric-based approaches require parameter tuning for different scenarios but require fewer computational resources. A self-tuned algorithm with high generalizability across diverse weather conditions is proposed in this paper. The algorithm integrates fuzzy logicbased adaptive functions with edge identification and line detection modules, enabling image adjustments in response to challenging weather conditions. The proposed tracking function utilizes previous detection results to fine-tune the selected Range of Interest (ROI), optimizing both accuracy and processing time. By incorporating these adaptive features into common geometric-based frameworks, the algorithm achieves higher detection rates compared to previous studies during challenging weather conditions. Furthermore, the proposed work exhibits better generalizability and significantly shorter processing time when compared to state-of-the-art learning-based models, as demonstrated through extensive testing on multiple datasets.

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“…Synthetic images can be easily manipulated to simulate various conditions, such as lighting, orientation, background, etc., that might not be readily available in real-world data. This enhances the ability of the model to generalize under different conditions[60][61][62][63]. •…”

mentioning

confidence: 97%

Rulers2023: An Annotated Dataset of Synthetic and Real Images for Ruler Detection Using Deep Learning

Matuzevičius

2023

Electronics

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This research investigates the usefulness and efficacy of synthetic ruler images for the development of a deep learning-based ruler detection algorithm. Synthetic images offer a compelling alternative to real-world images as data sources in the development and advancement of computer vision systems. This research aims to answer whether using a synthetic dataset of ruler images is sufficient for training an effective ruler detector and to what extent such a detector could benefit from including synthetic images as a data source. The article presents the procedural method for generating synthetic ruler images, describes the methodology for evaluating the synthetic dataset using trained convolutional neural network (CNN)-based ruler detectors, and shares the compiled synthetic and real ruler image datasets. It was found that the synthetic dataset yielded superior results in training the ruler detectors compared with the real image dataset. The results support the utility of synthetic datasets as a viable and advantageous approach to training deep learning models, especially when real-world data collection presents significant logistical challenges. The evidence presented here strongly supports the idea that when carefully generated and used, synthetic data can effectively replace real images in the development of CNN-based detection systems.

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Foggy Lane Dataset Synthesized from Monocular Images for Lane Detection Algorithms

Cited by 7 publications

References 51 publications

A Foggy Weather Simulation Algorithm for Traffic Image Synthesis Based on Monocular Depth Estimation

A Foggy Weather Simulation Algorithm for Traffic Image Synthesis Based on Monocular Depth Estimation

A Robust Lane Detection Algorithm Adaptable to Challenging Weather Conditions

Rulers2023: An Annotated Dataset of Synthetic and Real Images for Ruler Detection Using Deep Learning

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