Semantic Segmentation for Various Applications: Research Contribution and Comprehensive Review

Mazhar, Madiha; Fakhar, Saba; Rehman, Yawar

doi:10.3390/engproc2023032021

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…If it is necessary to take into account the relief of the analyzed surface and some more complex interconnections between neighboring pixels, it is advisable to employ more compound and advanced techniques of semantic image segmentation. In particular, such problems are well solved by deep learning methods, as shown by recent studies in the field of object detection and image segmentation [16][17][18][19][20][21]. Let us consider some state-of-the-art approaches.…”

Section: Related Workmentioning

confidence: 99%

“…Given the applied nature of the present research, we did not strive to compare all known approaches to the image segmentation problem. Based on the analysis of modern surveys [19][20][21][22], we have chosen the Fully Convolutional Networks (FCN) approach as the most appropriate for the described problem. One of the most popular, relatively simple, and effective options for the FCN architecture is the U-Net architecture, which was chosen to implement semantic segmentation.…”

Section: Related Workmentioning

confidence: 99%

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

Kamianskii,

Boldyrev,

Vezdenetsky

et al. 2023

Sensors

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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.

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Section: Related Workmentioning

confidence: 99%

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

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Advanced CNN Approach for Segmentation of Diseased Areas in Plant Images

Şener,

Ergen

2024

Journal of Crop Health

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A Review of Semantic Segmentation and Instance Segmentation Techniques in Forestry Using LiDAR and Imagery Data

Wołk,

Tatara

2024

Electronics

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The objective of this review is to conduct a critical analysis of the current literature pertaining to segmentation techniques and provide a methodical summary of their impact on forestry-related activities, emphasizing their applications using LiDAR and imagery data. This review covers the challenges, progress, and application of these strategies in ecological monitoring, forest inventory, and tree species classification. Through the process of synthesizing pivotal discoveries from multiple studies, this comprehensive analysis provides valuable perspectives on the present status of research and highlights prospective areas for further exploration. The primary topics addressed encompass the approach employed for executing the examination, the fundamental discoveries associated with semantic segmentation and instance segmentation in the domain of forestry, and the ramifications of these discoveries for the discipline. This review highlights the effectiveness of semantic and instance segmentation techniques in forestry applications, such as precise tree species identification and individual tree monitoring. However, challenges such as occlusions, overlapping branches, and varying data quality remain. Future research should focus on overcoming these obstacles to enhance the precision and applicability of these segmentation methodologies.

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Semantic Segmentation for Various Applications: Research Contribution and Comprehensive Review

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Cited by 3 publications

References 35 publications

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

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

Advanced CNN Approach for Segmentation of Diseased Areas in Plant Images

A Review of Semantic Segmentation and Instance Segmentation Techniques in Forestry Using LiDAR and Imagery Data

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