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International Journal of Engineering، جلد ۳۹، شماره ۱، صفحات ۱-۱۱
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عنوان انگلیسی Comparative Analysis of Image Segmentation Methods in Power Line Monitoring Systems
چکیده انگلیسی مقاله This study presents a comparative study of image segmentation methods for power line monitoring using unmanned aerial vehicle (UAV) imagery. The study investigates whether a hybrid segmentation pipeline—combining classical image processing and deep learning—can enhance defect detection under challenging conditions. Three traditional methods (Otsu thresholding, Canny/Sobel edge detection, and k-means clustering) and three deep learning models (UNet, DeepLabv3, Mask R-CNN) were evaluated on a dataset of annotated UAV images, including real and synthetically augmented scenes with lighting, noise, and weather variations. Performance was measured using Intersection over :union: (IoU), Pixel Accuracy, and processing time.Traditional methods demonstrated fast inference (0.2-0.4) but limited accuracy (IoU 0.47–0.58; Accuracy 72.5 –82%). Deep learning models significantly outperformed them: UNet achieved 0.85 IoU and 94% accuracy; DeepLabv3 reached 0.88 IoU and 96%; and Mask R-CNN led with 0.90 IoU and 97% accuracy, though at 1.2 seconds per image. The proposed hybrid method combines classical preprocessing for region-of-interest extraction with Mask R-CNN segmentation, achieving 0.89 IoU, 96.5% accuracy, and reduced processing time (0.75 s/image), improving speed by 30% with minimal accuracy loss. Robustness tests showed deep learning and hybrid methods degraded less than 6% under noise, compared to 20% for traditional methods. The results demonstrate that hybrid segmentation provides a practical balance between accuracy and efficiency, suitable for real-time monitoring on resource-limited platforms.
کلیدواژه‌های انگلیسی مقاله Image Segmentation,Power Line Monitoring,Deep Learning,Automated Systems,Segmentation Accuracy,defect detection
نویسندگان مقاله T. F. Tulyakov |
Department of System Analysis and Management of Empress Catherine II Saint Petersburg Mining University Saint Petersburg, Russia

O. V. Afanaseva |
Department of System Analysis and Management of Empress Catherine II Saint Petersburg Mining University Saint Petersburg, Russia

نشانی اینترنتی https://www.ije.ir/article_218639_e16cfa7fd0e631ac6e2db2b92963c59c.pdf
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