Review the state-of-the-art technologies of semantic segmentation based on deep learning

Mo, Yujian; Wu, Yan; Yang, Xinneng; Liu, Feilin; Liao, Yun

doi:10.1016/j.neucom.2022.01.005

Cited by 310 publications

(115 citation statements)

References 70 publications

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“…2 showing results of automatic ROI labeling. The algorithmic solution is based on the semantic segmentation [34], [35] that can predict the semantic category of each image pixel from a given set of labels using deep learning. Processing of these records in the Matlab environment includes:…”

Section: B Signal Processingmentioning

confidence: 99%

Blood Oxygen Concentration and Physiological Data Changes During Motion While Wearing Face Masks

et al. 2022

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The study of physiological changes recorded by wearable devices during physical exercises belongs to very important research topics in neurology for the detection of motion disorders or monitoring of the fitness level during sports activities. This paper contributes to this area with studies of the effect of face masks and respirators on blood oxygen concentration, breathing frequency, and the heart rate changes. Experimental data sets include 296 segments of their total length of 60 hours, recorded on a home exercise bike under different motion conditions. Wearable instruments with oximetric, heart rate, accelerometric, and thermal camera sensors were used to fill the own database of signals recorded with selected sampling frequencies. The proposed methodology includes fundamental signal and image processing methods for signal analysis and machine learning tools for labeling image components and detecting facial temperature changes. Results show the minimal effect of mask wearing on blood oxygen concentration but its substantial influence on the breathing frequency and the heart rate. The use of a respirator substantially increased the respiratory rate for the given set of experiments under the load. This indicates how wearable sensors, computational intelligence, and machine learning can be used for motion monitoring and data analysis of signals recorded in different conditions.

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Section: B Signal Processingmentioning

confidence: 99%

Blood Oxygen Concentration and Physiological Data Changes During Motion While Wearing Face Masks

et al. 2022

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“…Semantic segmentation based on deep learning is a robust method of regions of interest (ROI) discrimination, which provides the basis for the simulation map construction. [32] Meanwhile, the design of the reward function can contain multiple terms with different weights to regulate the print completion and repair quality. [31] To fulfill the proposed ideas, a suitable printing device should be developed to perform both structural and electrical repairs.…”

Section: Doi: 101002/aisy202200162mentioning

confidence: 99%

Artificial Intelligence‐Assisted Repair System for Structural and Electrical Restoration Using 3D Printing

Zhang

Qiao

Zhang

et al. 2022

Advanced Intelligent Systems

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The characteristic of material accumulation makes 3D printing competitive in remanufacturing and repairing. However, conventional repair methods require additional equipment and manual intervention to sequentially finish complicated processes such as global scanning and reverse modeling, which results in efficiency reduction and usage restriction. To address the existing shortcomings, an automatic repair system with artificial intelligence (AI) assistance is developed, which includes a semantic segmentation module, deep reinforcement learning (DRL) module, and composite printing device. The damaged part features are extracted by semantic segmentation from the captured real‐time images to establish DRL maps, where the print motion is simulated and transmitted to the printer. The results indicate that the applied bilateral segmentation network (BiSeNetV2) is 59.03% and 29.90% faster than pyramid scene parsing network (PSPNet) and DeepLabV3+ architecture with satisfying accuracy. The established DRL model based on actual printing achieves the optimization of agent learning speed and print quality. The automatic system improves the repair efficiency by 294% compared to the conventional methods, and enables both structural and electrical repair through high‐temperature polymer–metal printing. This intelligent system enables industrial robots to independently handle unexpected tasks in complex and changeable environments through interdisciplinary knowledge integration of advanced manufacturing and AI.

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“…In recent years, neural network models based on deep learning are being used increasingly and widely in a number of disciplines due to the increase in computing power and the advancement of various algorithms [19]. Among the algorithms related to image processing based on deep learning, semantic segmentation is a process whereby an input image is segmented into a number of meaningful image regions, and these segmented image regions are assigned labels [20]. The semantic segmentation image processing technique not only allows the segmentation of soil profile images but also the assignment of type labels to each of the segmented regions [21], thereby meeting the needs of soil diagnostic horizon delineation and identification in this study.…”

Section: Introductionmentioning

confidence: 99%

Toward Field Soil Surveys: Identifying and Delineating Soil Diagnostic Horizons Based on Deep Learning and RGB Image

2022

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The diagnostic horizon in a soil is reflective of the environment in which it developed and the inherent characteristics of the material, therefore quantitative approaches to horizon delineation should focus on the diagnostic horizon. Moreover, it can enable the exchange and transfer of soil information between different taxonomic systems. This study aims to explore the use of deep learning and RGB images to train a soil diagnostic horizon identification model that can help field workers determine soil horizon information quickly, efficiently, easily, and cost-effectively. A total of 331 soil profile images of the main soil categories (five soil orders, including Primosols, Ferrosols, Argosols, Anthrosols, and Cambosols) from Hubei and Jiangxi Provinces were used. Each soil profile image was preprocessed and augmented to 10 images and then inputted into the UNet++ architecture. The mean intersection over union and pixel accuracy of the model were 71.24% and 82.66%, respectively. Results show that the model could accurately identify and delineate the soil diagnostic horizons. Moreover, the model performance varied considerably due to the definition of the horizon and whether the diagnostic conditions applied to a wide range of visual features on RGB images, the number of samples, and the soil characteristics of the study area.

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Review the state-of-the-art technologies of semantic segmentation based on deep learning

Cited by 310 publications

References 70 publications

Blood Oxygen Concentration and Physiological Data Changes During Motion While Wearing Face Masks

Blood Oxygen Concentration and Physiological Data Changes During Motion While Wearing Face Masks

Artificial Intelligence‐Assisted Repair System for Structural and Electrical Restoration Using 3D Printing

Toward Field Soil Surveys: Identifying and Delineating Soil Diagnostic Horizons Based on Deep Learning and RGB Image

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