A Lightweight Dynamically Enhanced Network for Wildfire Smoke Detection in Transmission Line Channels

Processes, Год журнала: 2025, Номер 13(2), С. 349 - 349

Опубликована: Янв. 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.

Язык: Английский

Research and application of deep learning object detection methods for forest fire smoke recognition

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 10, 2025

Язык: Английский