DPMNet: A Remote Sensing Forest Fire Real-Time Detection Network Driven by Dual Pathways and Multidimensional Interactions of Features

Abstract

A fundamental challenge in remote sensing-based forest fire detection lies in accurately discerning fire characteristics on various scales against the backdrop of intricate and heterogeneous forest landscapes. In response to this challenge, we propose a dual-path network (DPMNet) with multidimensional feature interaction for real time remote sensing forest fire detection. Initially, a dual-path backbone network is designed, integrating coarse-grained and fine-grained parallel pathways, working in tandem to capture both global visual features and nuanced local texture details. Subsequently, we develop the Multidimensional Interactive Feature Pyramid Network (MiFPN), a novel structure that amalgamates information streams from varied levels through a three-branch structure and engenders profound fusion and dynamic interaction of features across multiple scales. Thereafter, the Context-Enriched Adaptive Fusion Module (CEAFM) is proposed, which emerges to meticulously blend macroscopic visual elements harvested via coarse-grained conduits, employing a multi-faceted pathway strategy to bolster the model’s overarching comprehension and precision in forest fire detection. Finally, the Enhanced Contextual Pooling Bottleneck (ECPB) is put forward, an integration that augments the model’s spatial perception and contextual acumen through the incorporation of dilated convolution and global pooling techniques. Extensive experiments are conducted on the remote sensing forest fire dataset in order to confirm the efficacy of DPMNet. The experimental results demonstrate that our DPMNet achieves satisfactory performance in terms of real-time performance as well as accuracy and provides an effective solution for real-time detection of remote sensing forest fires based on UAVs.