Insulators Identification for Overhead Transmission Lines in Distribution Networks Based on Multi-Scale Dense Network

Table 1. Number of different categories in the data set
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Table 1. Number of different categories in the data set
Category Total Train TrainVal Test
GS 5085 3051 762 1272
PS 8872 5323 1330 2219
PPin 2245 1347 336 562
PPost 5956 3573 893 1490
PC 6839 4103 1025 1711
R 4745 2847 711 1187

Table 2. Different strategies influence the results of the algorithm

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Table 2. Different strategies influence the results of the algorithm

Algorithm	Anchor	Input size	Dense Net	SPP	Multi-scale	Loss	GS /%	PS /%	PPin /%	PPost /%	PC /%	R /%	mAP /%	FPS /frame
YOLOv3							88.6	89.1	73.5	74.2	89.1	74.2	81.5	40
Improve1	√						89.2	90.6	75.3	76.5	90.3	76.6	83.1	41
Improve2	√	√					89.6	91.1	76.9	78.1	91.1	78.1	84.2	40
Improve3	√	√	√				91.8	92.8	82.9	81.8	93.9	81.5	87.5	34
Improve4	√	√	√	√			93.1	94.2	84.8	85.7	95.0	85.7	89.8	33
Improve5	√	√	√	√	√		94.3	95.7	86.4	87.3	97.3	88.2	91.5	31
Ours	√	√	√	√	√	√	96.1	96.6	88.5	89.8	98.1	90.9	93.4	28

Table 3. Test results of different algorithms

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Table 3. Test results of different algorithms

Algorithm	Backbone	Iterations	Precision /%	Recall /%	mAP /%	FPS /frame
Faster R-CNN	VGG16-Net	60000	79.1	77.5	74.6	7
SSD	VGG16-Net	60000	82.0	83.2	78.9	26
YOLOv3	DarkNet53	60000	83.7	83.5	81.5	40
RetinaNet	ResNet101	60000	84.8	84.3	82.4	20
Ours	DSM-DarkNet	60000	94.5	96.0	93.4	28

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Zhihao Chen, Yewei Xiao, Zhiqiang Li, Yang Liu. Insulators Identification for Overhead Transmission Lines in Distribution Networks Based on Multi-Scale Dense Network[J]. Laser & Optoelectronics Progress, 2021, 58(8): 0815003

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