Real-Time Smoke Detection Network Based on Multi-Scale Feature Recognition and Lightweight Architecture Design

Ganggang Li, Yongming Li, Shaochen Jiang

Abstract: Forest fires have significantly impacted global ecosystems and human societies, necessitating the development of efficient and accurate early smoke detection technologies for fires. However, current smoke detection technologies face multiple challenges in real-time applications, including large parameter size, high computational complexity, and low detection accuracy in complex scenes. Therefore, based on YOLOv8, we propose a lightweight, high-precision, real-time smoke detection network, MLSD(MultiScale Lightweight Smoke Detection Network). First, in order to reduce the computational complexity and the number of parameters of the model, we propose a lightweight detection head called EISDH(Efficient Information Sharing Detection Head). Second, to reduce the extraction of redundant features, we innovatively propose the C2f-PConv module. Third, to enhance the extraction capabilities of multi-scale and subtle smoke features in complex visual scenes, the downsampling module ADown was innovatively integrated into the model. MLSD demonstrates superior performance on three testing benchmarks. Notably, on the custom dataset FFES(Forest Fire Early Smoke Dataset), compared to the baseline, MLSD improves its mAP50 by 1.7% and its accuracy by 2.9% while reducing the number of parameters by 6.1M and decreasing GFLOPs by 12.4G.