Shadow Detection and Removal for Illumination Consistency on the Road

Wang, Chunxiang; Xu, Hanqing; Zhou, Zhiyu; Deng, Liuyuan; Yang, Ming

doi:10.1109/tiv.2020.2987440

Cited by 23 publications

(4 citation statements)

References 22 publications

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“…The proposed Parallel Light Fields framework divides light fields into three parts: digital twins of the light sources, virtual cameras, and light fields, which together constitute artificial systems. The effectiveness of these experiments was then evaluated by constructing several scenarios with various illumination by adjusting the intensity and direction of the light sources; a typical application is shadow detection and removal for illumination consistency by tuning directions of lights [ 18 ].…”

Section: Related Workmentioning

confidence: 99%

A New Parallel Intelligence Based Light Field Dataset for Depth Refinement and Scene Flow Estimation

Shen

Liu

Tian

et al. 2022

Sensors

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Computer vision tasks, such as motion estimation, depth estimation, object detection, etc., are better suited to light field images with more structural information than traditional 2D monocular images. However, since costly data acquisition instruments are difficult to calibrate, it is always hard to obtain real-world scene light field images. The majority of the datasets for static light field images now available are modest in size and cannot be used in methods such as transformer to fully leverage local and global correlations. Additionally, studies on dynamic situations, such as object tracking and motion estimates based on 4D light field images, have been rare, and we anticipate a superior performance. In this paper, we firstly propose a new static light field dataset that contains up to 50 scenes and takes 8 to 10 perspectives for each scene, with the ground truth including disparities, depths, surface normals, segmentations, and object poses. This dataset is larger scaled compared to current mainstream datasets for depth estimation refinement, and we focus on indoor and some outdoor scenarios. Second, to generate additional optical flow ground truth that indicates 3D motion of objects in addition to the ground truth obtained in static scenes in order to calculate more precise pixel level motion estimation, we released a light field scene flow dataset with dense 3D motion ground truth of pixels, and each scene has 150 frames. Thirdly, by utilizing the DistgDisp and DistgASR, which decouple the angular and spatial domain of the light field, we perform disparity estimation and angular super-resolution to evaluate the performance of our light field dataset. The performance and potential of our dataset in disparity estimation and angular super-resolution have been demonstrated by experimental results.

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Section: Related Workmentioning

confidence: 99%

A New Parallel Intelligence Based Light Field Dataset for Depth Refinement and Scene Flow Estimation

Shen

Liu

Tian

et al. 2022

Sensors

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“…Among the image enhancement principles, LCC has been shown to be more productive with fewer parameters, utilizing the target mean and variance as well as an intensity coefficient. LCC is widely used in automatic shadow compensation studies [24,25]. Chen et al [26] used LCC to compensate for the shadow area by combining the mean and variance in the shadow and non-shadow areas with a certain compensation coefficient.…”

Section: Related Workmentioning

confidence: 99%

An Automatic Shadow Compensation Method via a New Model Combined Wallis Filter with LCC Model in High Resolution Remote Sensing Images

et al. 2020

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Current automatic shadow compensation methods often suffer because their contrast improvement processes are not self-adaptive and, consequently, the results they produce do not adequately represent the real objects. The study presented in this paper designed a new automatic shadow compensation framework based on improvements to the Wallis principle, which included an intensity coefficient and a stretching coefficient to enhance contrast and brightness more efficiently. An automatic parameter calculation strategy also is a part of this framework, which is based on searching for and matching similar feature points around shadow boundaries. Finally, a final compensation combination strategy combines the regional compensation with the local window compensation of the pixels in each shadow to improve the shaded information in a balanced way. All these strategies in our method work together to provide a better measurement for customizing suitable compensation depending on the condition of each region and pixel. The intensity component I also is automatically strengthened through the customized compensation model. Color correction is executed in a way that avoids the color bias caused by over-compensated component values, thereby better reflecting shaded information. Images with clouds shadows and ground objects shadows were utilized to test our method and six other state-of-the-art methods. The comparison results indicate that our method compensated for shaded information more effectively, accurately, and evenly than the other methods for customizing suitable models for each shadow and pixel with reasonable time-cost. Its brightness, contrast, and object color in shaded areas were approximately equalized with non-shaded regions to present a shadow-free image.

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“…This has effectively improved the reliability and accuracy of crack detection. Full convolutional neural networks (FCN) have been widely used in road detection tasks [8][9][10] and have achieved state-of-the-art (SOTA) performance. Some works treat crack detection as a segmentation task based on advanced network models such as U-Net [11] or SegNet [12].…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Network with Dual Encoder and Cross Feature Fusion for Cement Pavement Crack Detection

Qu,

Mu,

Yuan

2024

CMES

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Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning, with convolutional neural networks (CNN) playing an important role in this field. However, as the performance of crack detection in cement pavement improves, the depth and width of the network structure are significantly increased, which necessitates more computing power and storage space. This limitation hampers the practical implementation of crack detection models on various platforms, particularly portable devices like small mobile devices. To solve these problems, we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms for more efficient feature fusion. Firstly, we use small channel convolution to construct shallow feature extraction module (SFEM) to extract low-level feature information of cracks in cement pavement images, in order to obtain more information about cracks in the shallow features of images. In addition, we construct large kernel atrous convolution (LKAC) to enhance crack information, which incorporates coordination attention mechanism for non-crack information filtering, and large kernel atrous convolution with different cores, using different receptive fields to extract more detailed edge and context information. Finally, the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module, and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction map. We evaluate our method on three public crack datasets: DeepCrack, CFD, and Crack500. Experimental results on the DeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods, which achieves Precision (P) 87.2%, Recall (R) 87.7%, and F-score (F 1 ) 87.4%. Thanks to our lightweight crack detection model, the parameter count of the model in real-world detection scenarios has been significantly reduced to less than 2M. This advancement also facilitates technical support for portable scene detection.

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Shadow Detection and Removal for Illumination Consistency on the Road

Cited by 23 publications

References 22 publications

A New Parallel Intelligence Based Light Field Dataset for Depth Refinement and Scene Flow Estimation

A New Parallel Intelligence Based Light Field Dataset for Depth Refinement and Scene Flow Estimation

An Automatic Shadow Compensation Method via a New Model Combined Wallis Filter with LCC Model in High Resolution Remote Sensing Images

A Lightweight Network with Dual Encoder and Cross Feature Fusion for Cement Pavement Crack Detection

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