A novel deep learning network for accurate lane detection in low-light environments

Song, Shuang; Chen, Wei; Liu, Qianjie; Hu, Huosheng; Huang, Tengchao; Zhu, Qingyuan

doi:10.1177/09544070211016254

Cited by 11 publications

(7 citation statements)

References 40 publications

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“…Low-light image enhancement can be used in imaged-based 3D reconstruction in low-light conditions [10]. Enhancing low-light images for lane detection under low-light environments plays a significant role in advanced driver assistance systems (ADAS) [11]. Therefore, the exploration of low-light image enhancement has emerged as a challenging and dynamically evolving research domain [12].…”

Section: Introductionmentioning

confidence: 99%

LLE-NET: A Low-Light Image Enhancement Algorithm Based on Curve Estimation

Cao,

2024

Mathematics

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Low-light image enhancement is very significant for vision tasks. We introduce Low-light Image Enhancement via Deep Learning Network (LLE-NET), which employs a deep network to estimate curve parameters. Cubic curves and gamma correction are employed for enhancing low-light images. Our research trains a lightweight network to estimate the parameters that determine the correction curve. By the results of the deep learning network, accurate correction curves are confirmed, which are used for the per-pixel correction of RGB channels. The image enhanced by our models closely resembles the input image. To further accelerate the inferring speed of the low-light enhancement model, a low-light enhancement model based on gamma correction is proposed with one iteration. LLE-NET exhibits remarkable inference speed, achieving 400 fps on a single GPU for images sized 640×480×3 while maintaining pleasing enhancement quality. The enhancement model based on gamma correction attains an impressive inference speed of 800 fps for images sized 640×480×3 on a single GPU.

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Section: Introductionmentioning

confidence: 99%

LLE-NET: A Low-Light Image Enhancement Algorithm Based on Curve Estimation

Cao,

2024

Mathematics

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“…Borkar, et al(2009) made the layered approach method for detecting lane lines at night, they firstly extract the regions from a lane line image, then make the image enhancement based on lane shape, subsequently the image of the region is threhsolded, and the lane lines are detected roughly, and finally the lane lines are identified 31 . Song, et al (2021) studied a deep learning method for the lane detection in low-light situation, in the method, a lane line image is firstly enhanced by a neural network model, then the lane lines are extracted by a deep learning method, and finally the lane lines are identified by applying the KD tree models 32 .…”

Section: Introductionmentioning

confidence: 99%

Lane line detection at nighttime on fractional differential and central line point searching with Fragi and Hessian

Wang

et al. 2023

Sci Rep

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To detect lanes at night, each detecting image is the fusion of the multiple images in a video sequence. The valid lane line detection region is identified on region merging. Then, the image preprocessing algorithm based on the Fragi algorithm and Hessian matrix is applied to enhance lanes; to extract the lane line center feature points, the image segmentation algorithm based on Fractional differential is proposed; and according to the possible lane line positions, the algorithm detects the centerline points in four directions. Subsequently, the candidate points are determined, and the recursive Hough transformation is applied to obtain the possible lane lines. Finally, to obtain the final lane lines, we assume that one lane line should have an angle between 25 and 65 degrees, while the other should have an angle between 115 and 155 degrees, if the detected line is not in the regions, the Hough line detection will be continued by increasing the threshold value until the two lane lines are got. By testing more than 500 images and comparing deep learning methods and image segmentation algorithms, the lane detection accuracy by the new algorithm is up to 70%.

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“…Producing a naturally robust database is a very time-consuming and expensive undertaking. Recent research works on autonomous vehicles and influential road factors have focused on improving the performance of object detectors in difficult and challenging environmental conditions [15][16][17]. One of the main approaches for creating a robust dataset is to use data augmentation techniques.…”

Section: Introductionmentioning

confidence: 99%

Road damage detection with bounding box and generative adversarial networks based augmentation methods

Aghayan‐Mashhady,

Amirkhani

2023

IET Image Processing

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In this paper, based on the data augmentation techniques of bounding box augmentation and the road damage generative adversarial network based augmentation, a robust road damage detection method has been presented. To this end, first, Iran road damage dataset has been collected by means of a dashboard‐installed mobile phone. After processing these images by the blind referenceless image spatial quality evaluator technique, the substandard and inferior data have been automatically eliminated. In the second step, based on the YOLOv5 with several different baseline models, an algorithm has been developed for detecting the road surface damages. In the third step, by using the traditional as well as the bounding box augmentation and road damage generative adversarial network based augmentation techniques, the precision and the robustness of road damage detector under different environmental and field conditions have been improved. Finally, through the ensemble of the best models, the final detector accuracy has been enhanced. The findings of this article indicate that by using the proposed approach, the values of mAP and F1‐score are improved by 13.79% and 7.58%, respectively. The dataset and parts of the code are available at: https://github.com/IranRoadDamageDataset/IRRDD.

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A novel deep learning network for accurate lane detection in low-light environments

Cited by 11 publications

References 40 publications

LLE-NET: A Low-Light Image Enhancement Algorithm Based on Curve Estimation

LLE-NET: A Low-Light Image Enhancement Algorithm Based on Curve Estimation

Lane line detection at nighttime on fractional differential and central line point searching with Fragi and Hessian

Road damage detection with bounding box and generative adversarial networks based augmentation methods

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