SUN S M, TANG Y H, TAI T,et al. Lightweight image segmentation method for transmission line insulators based on MobileNetV2[J].Journal of Henan Polytechnic University(Natural Science) ,2025,44(5):35-42.

DOI:10.16186/j.cnki.1673-9787.2024070039

Received: 2024/07/09

Revised: 2024/10/02

Published:2025/07/23

Lightweight image segmentation method for transmission line insulators based on MobileNetV2

Sun Shiming1, Tang Yuanhe1, Tai Tong1, Wei Xueyun1, Fang Wei2

1.NARI-TECH Nanjing Control Systems Co.， Ltd.， Nanjing  211106， Jiangsu， China；2.School of Computer Science， Nanjing University of Information Science & Technology， Nanjing  210044， Jiangsu， China

Abstract: Objectives To address the issues of low accuracy in insulator segmentation in aerial images of transmission line inspections， limited computing power of edge devices， large model parameters， and insufficient real-time performance， a lightweight transmission line insulator segmentation network （ISNet） based on MobileNetV2 was proposed.  Methods Firstly， a lightweight MobileNetV2 was used as the encoder backbone network to re extract multi-scale features from the input image； Secondly， a new diverse feature aggregation module （DFAM） was proposed， which aggregated diverse spatial position information and advanced semantic information through convolutional layers with different convolution kernels， channel attention， and spatial attention mechanisms； Finally， a multi-level symmetric decoder （MSD） was designed to fuse the output features from the same layer encoder and the previous decoder to generate the final prediction image.  Results The experimental results showed that the proposed method achieved excellent performance on the aerial image insulator segmentation dataset. In terms of mIoU index， ISNet reached 90.9%， which was 5.2% and 1.2% higher than DeepLabV3plus and SegFormer， respectively； On the mPA metric， ISNet achieved 93.6%， which was 5.2% and 0.8% higher than DeepLabV3plus and SegFormer， respectively； In addition， the proposed method ISNet could achieve an inference speed of 71.2 F/s on a single NVIDIA RTX 3090 GPU， with only 3.1 M of parameters and 21.2 G of floating-point operations （FLOPs） （input image size of 1 024 × 1 024）， which was superior to current mainstream semantic segmentation methods. Conclusions In summary， the proposed method ISNet achieved the best segmentation accuracy while improving the lightweighting and real-time performance of the model.

Key words:MobileNetV2;semantic segmentation;insulator;transmission line inspection;deep learning;computer vision