Person identification with aerial imaginary using SegNet based semantic segmentation

Manickam, Rajeswari; Rajan, Satheesh Kumar; Subramanian, Chidambaranathan; Xavi, Arnold; Eanoch, Golden Julie; Robinson, Y. Harold

doi:10.1007/s12145-020-00516-y

Cited by 20 publications

(10 citation statements)

References 20 publications

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“…Therefore, training of U-Net is relatively slow compared to SegNet ( 59 ). Moreover, in the study by Manickam et al ( 60 ), in which semantic human detection from UAV images was made, SegNet provided more successful results than many other deep models. Similarly, in another study for brain tissue segmentation by Kumar et al ( 59 ), SegNet provided more successful classification than U-Net.…”

Section: Experimental Studies and Resultsmentioning

confidence: 98%

COVID-19 Isolation Control Proposal via UAV and UGV for Crowded Indoor Environments: Assistive Robots in the Shopping Malls

Aslan

Hasikin‬

Yusefi

et al. 2022

Front. Public Health

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Artificial intelligence researchers conducted different studies to reduce the spread of COVID-19. Unlike other studies, this paper isn't for early infection diagnosis, but for preventing the transmission of COVID-19 in social environments. Among the studies on this is regarding social distancing, as this method is proven to prevent COVID-19 to be transmitted from one to another. In the study, Robot Operating System (ROS) simulates a shopping mall using Gazebo, and customers are monitored by Turtlebot and Unmanned Aerial Vehicle (UAV, DJI Tello). Through frames analysis captured by Turtlebot, a particular person is identified and followed at the shopping mall. Turtlebot is a wheeled robot that follows people without contact and is used as a shopping cart. Therefore, a customer doesn't touch the shopping cart that someone else comes into contact with, and also makes his/her shopping easier. The UAV detects people from above and determines the distance between people. In this way, a warning system can be created by detecting places where social distance is neglected. Histogram of Oriented-Gradients (HOG)-Support Vector Machine (SVM) is applied by Turtlebot to detect humans, and Kalman-Filter is used for human tracking. SegNet is performed for semantically detecting people and measuring distance via UAV. This paper proposes a new robotic study to prevent the infection and proved that this system is feasible.

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Section: Experimental Studies and Resultsmentioning

confidence: 98%

COVID-19 Isolation Control Proposal via UAV and UGV for Crowded Indoor Environments: Assistive Robots in the Shopping Malls

Aslan

Hasikin‬

Yusefi

et al. 2022

Front. Public Health

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show abstract

“…The SegNet model outperformed the other two models and had an overall segmentation accuracy of 92.7%. Similarly, Manickam et al [69] compared four models (VGG16, GoogleNet, ResNet, and SegNet) for personal identification with aerial imaginary. Their results revealed that SegNet achieved the highest overall accuracy of 91.04%.…”

Section: Evaluation Of Segmentation Methodsmentioning

confidence: 99%

Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics

et al. 2022

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In the aquaponic system, plant nutrients bioavailable from fish excreta are not sufficient for optimal plant growth. Accurate and timely monitoring of the plant’s nutrient status grown in aquaponics is a challenge in order to maintain the balance and sustainability of the system. This study aimed to integrate color imaging and deep convolutional neural networks (DCNNs) to diagnose the nutrient status of lettuce grown in aquaponics. Our approach consists of multi-stage procedures, including plant object detection and classification of nutrient deficiency. The robustness and diagnostic capability of proposed approaches were evaluated using a total number of 3000 lettuce images that were classified into four nutritional classes—namely, full nutrition (FN), nitrogen deficiency (N), phosphorous deficiency (P), and potassium deficiency (K). The performance of the DCNNs was compared with traditional machine learning (ML) algorithms (i.e., Simple thresholding, K-means, support vector machine; SVM, k-nearest neighbor; KNN, and decision Tree; DT). The results demonstrated that the deep proposed segmentation model obtained an accuracy of 99.1%. Also, the deep proposed classification model achieved the highest accuracy of 96.5%. These results indicate that deep learning models, combined with color imaging, provide a promising approach to timely monitor nutrient status of the plants grown in aquaponics, which allows for taking preventive measures and mitigating economic and production losses. These approaches can be integrated into embedded devices to control nutrient cycles in aquaponics.

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“…In the Encoder Network Layer of SegNet, the pre-trained VGG16 network is used to remove the full connection layer, leaving only the previous convolutional layers as the coding layer. The main reason for this is to maintain higher resolution and reduce network parameters to improve training efficiency [47]. There is a novel Hierarchical Transformer Encoder in the SegFormer network architecture, which enables the network to output multi-scale features and does not require location coding.…”

Section: Classifier Model Architecturesmentioning

confidence: 99%

Development and application of a more refined process for extracting rock crack width information based on artificial intelligence

Ye¹,

Yu²,

Zhang

et al. 2023

Preprint

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The process of image collection of high-altitude rock cracks using unmanned aerial vehicle (UAV) suffers from insufficient resolution and motion blur, which prevents more accurate detection of micro-cracks. Therefore, in this study, a rock crack refinement detection process (RC-RDP) based on super-resolution reconstruction (SRR) technique and semantic segmentation (SS) network is developed to detect micro-cracks. Four SRR networks (RCAN, SRDenseNet, ESRGAN, BSRGAN) and six SS networks (PSPNet, SegNet, DeepLab V3+, UNet++, UNet++(CBAM), SegFormer) are trained and tested separately using rock crack datasets. SRR indicators (PSNR and SSIM) and SS indicators (Precision, Recall, F1-Score and IoU) are applied to evaluate the performance of SRR networks and SS networks, respectively. According to the evaluation indicators of each network performance, in this paper, the RCAN network (PSNR = 31.08 dB and SSIM = 88.56%) is applied in the SRR part, and the UNet++ (CBAM) network is used in the crack detection part (Precision = 0.874, Recall = 0.886, F1-Score = 0.879 and IoU = 0.785). In addition, a novel data acquisition process integrating skeletonization algorithm, feature nodes detection algorithm, normal vector estimation algorithm and width extraction algorithm is used to calculate the minimum width WMin, maximum width WMax and average width WA information of the crack traces. The results of this paper show that the application of RC-RDP based on deep learning can obtain better super resolution (SR) images and more accurate width information. The method in this study shows excellent performance in detecting rock cracks.

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Person identification with aerial imaginary using SegNet based semantic segmentation

Cited by 20 publications

References 20 publications

COVID-19 Isolation Control Proposal via UAV and UGV for Crowded Indoor Environments: Assistive Robots in the Shopping Malls

COVID-19 Isolation Control Proposal via UAV and UGV for Crowded Indoor Environments: Assistive Robots in the Shopping Malls

Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics

Development and application of a more refined process for extracting rock crack width information based on artificial intelligence

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