Deep Learning Model In Road Surface Condition Monitoring

Robet, Robet; Hasibuan, Zainal A.; Soeleman, Mochammad Arief; Purwanto, Purwanto; Andono, Pulung Nurtantio; Pujiono, Pujiono

doi:10.1109/isemantic55962.2022.9920464

Cited by 4 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, Robet et al [26] used a U-Net-based neural network to tackle the task of semantically segmenting the roadway for pavement type determination and defect detection. The specialty of the approach is that images for analysis are captured by a road surveillance camera (the Road Traversing Knowledge (RTK) Dataset is used for training).…”

Section: Related Workmentioning

confidence: 99%

Design and Implementation of a Hardware and Software System for Visual Assessment of Bituminous Coating Quality

Kamianskii,

Boldyrev,

Vezdenetsky

et al. 2023

Sensors

View full text Add to dashboard Cite

Typically, the quality of the bitumen adhesion in asphalt mixtures is assessed manually by a group of experts who assign subjective ratings to the thickness of the residual bitumen coating on the gravel samples. To automate this process, we propose a hardware and software system for visual assessment of bituminous coating quality, which provides the results both in the form of a discrete estimate compatible with the expert one, and in a more general percentage for a set of samples. The developed methodology ensures static conditions of image capturing, insensitive to external circumstances. This is achieved by using a hardware construction designed to provide capturing the samples at eight different illumination angles. As a result, a generalized image is obtained, in which the effect of highlights and shadows is eliminated. After preprocessing, each gravel sample independently undergoes surface semantic segmentation procedure. Two most relevant approaches of semantic image segmentation were considered: gradient boosting and U-Net architecture. These approaches were compared by both stone surface segmentation accuracy, where they showed the same 77% result and the effectiveness in determining a discrete estimate. Gradient boosting showed an accuracy 2% higher than the U-Net for it and was thereby chosen as the main model when developing the prototype. According to the test results, the evaluation of the algorithm in 75% of cases completely coincided with the expert one, and it had a slight deviation from it in another 22% of cases. The developed solution allows for standardizing the data obtained and contributes to the creation of an interlaboratory digital research database.

show abstract

Section: Related Workmentioning

confidence: 99%

Design and Implementation of a Hardware and Software System for Visual Assessment of Bituminous Coating Quality

Kamianskii,

Boldyrev,

Vezdenetsky

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…With the technical progress of general-purpose deep learning methods [2], many researchers apply deep learning-based detection methods to the task of detecting road damage, for instance image classification [3,4], target detection [5][6][7], and semantic segmentation [8][9][10][11]. These algorithms are effective in the detection of road damage [12][13][14]. Among them, the detection method based on image classification is to first segment the original image into sub-image fast, then judge these sub-image blocks by using a binary classification network; a final step involves stitching these sub-image modules into the original image.…”

Section: Introductionmentioning

confidence: 99%

Dense Multiscale Feature Learning Transformer Embedding Cross-Shaped Attention for Road Damage Detection

Zhang

Mei

et al. 2023

Electronics

View full text Add to dashboard Cite

Road damage detection is essential to the maintenance and management of roads. The morphological road damage contains a large number of multi-scale features, which means that existing road damage detection algorithms are unable to effectively distinguish and fuse multiple features. In this paper, we propose a dense multiscale feature learning Transformer embedding cross-shaped attention for road damage detection (DMTC) network, which can segment the damage information in road images and improve the effectiveness of road damage detection. Our DMTC makes three contributions. Firstly, we adopt a cross-shaped attention mechanism to expand the perceptual field of feature extraction, and its global attention effectively improves the feature description of the network. Secondly, we use the dense multi-scale feature learning module to integrate local information at different scales, so that we are able to overcome the difficulty of detecting multiscale targets. Finally, we utilize a multi-layer convolutional segmentation head to generalize the previous feature learning and get a final detection result. Experimental results show that our DMTC network could segment pavement pothole patterns more accurately and effectively than other methods, achieving an F1 score of 79.39% as well as an OA score of 99.83% on the cracks-and-potholes-in-road-images-dataset (CPRID).

show abstract