• KIPS Transactions on Computer and Communication Systems
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• v.12 no.2
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• pp.85-92
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• 2023

Respiratory infections such as COVID-19 mainly occur within enclosed spaces. The presence or absence of abnormal symptoms of respiratory infectious diseases is judged through initial symptoms such as fever, cough, sneezing and difficulty breathing, and constant monitoring of these early symptoms is required. In this paper, image matching correction was performed for the RGB camera module and the thermal imaging camera module, and the temperature of the thermal imaging camera module for the measurement environment was calibrated using a blackbody. To detection the target recommended by the standard, a deep learning-based object recognition algorithm and the inner canthus recognition model were developed, and the model accuracy was derived by applying a dataset of 100 experimenters. Also, the error according to the measured distance was corrected through the object distance measurement using the Lidar module and the linear regression correction module. To measure the performance of the proposed model, an experimental environment consisting of a motor stage, an infrared thermography temperature screening system and a blackbody was established, and the error accuracy within 0.28℃ was shown as a result of temperature measurement according to a variable distance between 1m and 3.5 m.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1195-1210
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• 2023

Coastal debris has emerged as a salient issue due to its adverse effects on coastal aesthetics, ecological systems, and human health. In pursuit of effective countermeasures, the present study delineated the construction of a specialized image dataset for coastal debris detection and embarked on a comparative analysis between two paramount real-time object detection algorithms, YOLOv8 and RT-DETR. Rigorous assessments of robustness under multifarious conditions were instituted, subjecting the models to assorted distortion paradigms. YOLOv8 manifested a detection accuracy with a mean Average Precision (mAP) value ranging from 0.927 to 0.945 and an operational speed between 65 and 135 Frames Per Second (FPS). Conversely, RT-DETR yielded an mAP value bracket of 0.917 to 0.918 with a detection velocity spanning 40 to 53 FPS. While RT-DETR exhibited enhanced robustness against color distortions, YOLOv8 surpassed resilience under other evaluative criteria. The implications derived from this investigation are poised to furnish pivotal directives for algorithmic selection in the practical deployment of marine debris monitoring systems.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.297-309
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• 2023

The degradation of coastal ecosystems and fishery environments is accelerating due to the recent phenomenon of invertebrate grazers. To effectively monitor and implement preventive measures for this phenomenon, the adoption of remote sensing-based monitoring technology for extensive maritime areas is imperative. In this study, we compared and analyzed the robustness of deep learning-based object detection modelsfor detecting and monitoring invertebrate grazersfrom underwater videos. We constructed an image dataset targeting seven representative species of invertebrate grazers in the coastal waters of South Korea and trained deep learning-based object detection models, You Only Look Once (YOLO)v7 and YOLOv8, using this dataset. We evaluated the detection performance and speed of a total of six YOLO models (YOLOv7, YOLOv7x, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x) and conducted robustness evaluations considering various image distortions that may occur during underwater filming. The evaluation results showed that the YOLOv8 models demonstrated higher detection speed (approximately 71 to 141 FPS [frame per second]) compared to the number of parameters. In terms of detection performance, the YOLOv8 models (mean average precision [mAP] 0.848 to 0.882) exhibited better performance than the YOLOv7 models (mAP 0.847 to 0.850). Regarding model robustness, it was observed that the YOLOv7 models were more robust to shape distortions, while the YOLOv8 models were relatively more robust to color distortions. Therefore, considering that shape distortions occur less frequently in underwater video recordings while color distortions are more frequent in coastal areas, it can be concluded that utilizing YOLOv8 models is a valid choice for invertebrate grazer detection and monitoring in coastal waters.