轻量级网络算法对输电线路上异物目标的识别

doi:10.16180/j.cnki.issn1007-7820.2023.04.010

Abstract

Abstract:

In view of the power inspection problem caused by various foreign objects on the transmission line, the deep learning image recognition method can be used for detection. This study proposes an improved lightweight network detection algorithm model. By replacing the backbone feature extraction network of YOLOv4 with lightweight neural network GhostNet, the redundancy of feature map generated by image input calculation is reduced. The PANet module of YOLOv4 is modified, and the depth separable convolution module is used to replace the common convolution module, which can reduce the amount of parameter calculation. The results show that, compared with the original YOLOv4 detection algorithm, when the IOU threshold is 0.5, the average accuracy of the improved algorithm decreases by 2.1%, but the detection speed is 2.21 times that of the original algorithm, and the parameter calculation amount is only 17.84% of the original algorithm. The comparison with other algorithms shows that the parameter performance of the proposed algorithm meets the demand. Under the condition of maintaining high accuracy, the detection speed of the proposed algorithm is improved and the computation amount is reduced, which proves the effectiveness and feasibility of the proposed algorithm in target detection.

Key words: transmission line, deep learning, YOLOv4, image recognition, GhostNet, deep separable convolution module, lightweight network

CLC Number:

TP391.4

TANG Zheng,ZHANG Huilin,MA Lixin,LIU Jinzhi,WANG Hao. Identification of Foreign Objects on Transmission Lines Using Lightweight Network Algorithm[J].Electronic Science and Technology, 2023, 36(4): 71-77.

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References 18

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参数名称	参数值
输入尺寸	608×608
训练次数	100
训练集与测试集比例划分	9∶1
Batch_size	12
初始学习率	0.001

GhostNet	深度可分离卷积	mAP/%	FPS/frames·s^-1	FLOPs
		98.04	12.05	64 363 101
√		96.35	16.32	39 689 409
	√	94.12	17.12	36 102 237
√	√	95.94	26.67	11 428 545

算法模型	mAP/%	FPS/ frames·s^-1	FLOPs
本文算法	95.94	26.67	11 428 545
YOLOv4	98.04	12.05	64 363 101
YOLOv3	68.59	25.31	61 949 149
YOLOv4-tiny	73.35	72.31	6 102 237
文献[17]	88.82	6.21	137 078 239
文献[18]	83.76	24.03	8 700 432

Identification of Foreign Objects on Transmission Lines Using Lightweight Network Algorithm

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