A Lightweight Dynamically Enhanced Network for Wildfire Smoke Detection in Transmission Line Channels DOI Open Access
Yu Zhang,

Yangyang Jiao,

Yinke Dou

et al.

Processes, Journal Year: 2025, Volume and Issue: 13(2), P. 349 - 349

Published: Jan. 27, 2025

In view of the problems that mean existing detection networks are not effective in detecting dynamic targets such as wildfire smoke, a lightweight dynamically enhanced transmission line channel smoke network LDENet is proposed. Firstly, Dynamic Lightweight Conv Module (DLCM) devised within backbone YOLOv8 to enhance perception flames and through convolution. Then, Ghost used model. DLCM reduces number model parameters improves accuracy detection. DySample upsampling operator part make image generation more accurate with very few parameters. Finally, course training process, loss function improved. EMASlideLoss improve ability for small targets, Shape-IoU optimize shape wildfires smoke. Experiments conducted on datasets, final mAP50 86.6%, which 1.5% higher than YOLOv8, decreased by 29.7%. The experimental findings demonstrate capable effectively ensuring safety corridors.

Language: Английский

A Lightweight Dynamically Enhanced Network for Wildfire Smoke Detection in Transmission Line Channels DOI Open Access
Yu Zhang,

Yangyang Jiao,

Yinke Dou

et al.

Processes, Journal Year: 2025, Volume and Issue: 13(2), P. 349 - 349

Published: Jan. 27, 2025

In view of the problems that mean existing detection networks are not effective in detecting dynamic targets such as wildfire smoke, a lightweight dynamically enhanced transmission line channel smoke network LDENet is proposed. Firstly, Dynamic Lightweight Conv Module (DLCM) devised within backbone YOLOv8 to enhance perception flames and through convolution. Then, Ghost used model. DLCM reduces number model parameters improves accuracy detection. DySample upsampling operator part make image generation more accurate with very few parameters. Finally, course training process, loss function improved. EMASlideLoss improve ability for small targets, Shape-IoU optimize shape wildfires smoke. Experiments conducted on datasets, final mAP50 86.6%, which 1.5% higher than YOLOv8, decreased by 29.7%. The experimental findings demonstrate capable effectively ensuring safety corridors.

Language: Английский

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