Wire recognition in image within aerial inspection application

Koshelev, V. I.; Kozlov, Dmitry N.

doi:10.1109/meco.2015.7181891

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…Moreover, the places they pass through are generally sparsely populated and difficult to access. On this basis, intelligent inspection technology [5][6][7][8] was developed to inspect transmission lines through aerial photography by UAVs, which can save a lot of human resources and material resources with high-detection efficiency compared with the manual labor. However, the data from aerial photography still need to be distinguished by human judgement, so the detection efficiency and accuracy of the whole process still need to be improved.…”

Section: Introductionmentioning

confidence: 99%

Object Detection Algorithm of Transmission Lines Based on Improved YOLOv5 Framework

Zhang,

Zhou,

Shi

et al. 2024

Journal of Sensors

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Foreign objects easily attach to the transmission lines because of the various laying methods and the complex, changing environment. They have a significant impact on the safe operation capability of transmission lines if these foreign objects are not detected and removed in time. An improved YOLOv5 technique is provided to detect foreign objects in transmission lines due to the low-foreign object recognition accuracy image detection. The method first reduces the computation and memory consumption by introducing the RepConv structure, further improves the detection accuracy and speed of the model by embedding the C2F structure. This method finally is further optimized neural network by the Meta-ACON activation function. The results indicate that the average detection accuracy of the improved YOLOv5 network can reach 96.9%, which is 2.2% higher than before. Additionally, corresponding detection speed can reach 258.36 frames/second, which surpasses existing mainstream target detection models, performing better in terms of the balance of inference speed and detection accuracy. Consequently, the effectiveness and superiority of the algorithm have been proved.

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Section: Introductionmentioning

confidence: 99%

Object Detection Algorithm of Transmission Lines Based on Improved YOLOv5 Framework

Zhang,

Zhou,

Shi

et al. 2024

Journal of Sensors

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“…Once any security issues must be settled physically, reaction time will be limited [4]. Result of this are, brilliantly assessment innovation [5][6][7][8] was made by utilizing UAV airborne photography of transmission lines for review, which can spare a part of time and cash whereas moreover having a tall location proficiency when compared to fake methods RCNN [9]. GPU computing control is growing in this enormous information period, and profound learning is continuously appearing to be valuable in numerous computer visioned apps, especially target location employments.…”

Section: Introductionmentioning

confidence: 99%

Object Detection in Electricity Cables With Yolo-X Algorithm

2023

IRJMETS

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Since of the incredible differences of laying and the complicated, changing environment, outsider things can effortlessly connect themselves to transmission line passages. In the event that these outside things are not found and expelled instantly, they can have a serious affect on the capacity of transmission lines to run safely. We offer an upgraded YOLOX approach for identifying outside things in transmission lines in reaction to the issue of destitute precision of remote protest recognizable proof in transmission line picture review.

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“…Processes for manual operation and maintenance are also very costly, time-consuming, and challenging to finish on schedule. As a result, by using UAV aerial photography of transmission lines for inspection, intelligent inspection technology [5][6][7][8] was developed, which can save a lot of time and resources while also having a high detection efficiency when compared to artificial techniques. To recognize aerial data, nevertheless, still requires manual judgment.…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOX Foreign Object Detection Algorithm for Transmission Lines

Guo

Chen

et al. 2022

Wireless Communications and Mobile Computing

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It is quite simple for foreign objects to attach themselves to transmission line corridors because of the wide variety of laying and the complex, changing environment. If these foreign objects are not found and removed in a timely manner, they can have a significant impact on the transmission lines’ ability to operate safely. Due to the problem of poor accuracy of foreign object identification in transmission line image inspection, we provide an improved YOLOX technique for detection of foreign objects in transmission lines. The method improves the YOLOX target detection network by first using Atrous Spatial Pyramid Pooling to increase sensitivity to foreign objects of different scales, then by embedding Convolutional Block Attention Module to increase model recognition accuracy, and finally by using GIoU loss to further optimize. The testing findings show that the enhanced YOLOX network has a mAP improvement of around 4.24% over the baseline YOLOX network. The target detection SSD, Faster R-CNN, YOLOv5, and YOLOV7 networks have improved less than this. The effectiveness and superiority of the algorithm are proven.

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Wire recognition in image within aerial inspection application

Cited by 8 publications

References 6 publications

Object Detection Algorithm of Transmission Lines Based on Improved YOLOv5 Framework

Object Detection Algorithm of Transmission Lines Based on Improved YOLOv5 Framework

Object Detection in Electricity Cables With Yolo-X Algorithm

Improved YOLOX Foreign Object Detection Algorithm for Transmission Lines

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