Deep learning-based forest fire detection using an improved SSD algorithm with CBAM

Fires are characterized by their sudden onset, rapid spread, and destructive nature, often causing irreversible damage to ecosystems. To address the challenges in forest fire detection, including the varying scales and complex features of flame and smoke, as well as false positives and missed detections caused by environmental interference, we propose a novel object detection model named CBAM-SSD. Firstly, data augmentation techniques involving geometric and color transformations are employed to enrich the dataset, effectively mitigating issues of insufficient and incomplete data collected in real-world scenarios. This significantly enhances the SSD model's ability to detect flames, which exhibit highly variable morphological characteristics. Furthermore, the CBAM module is integrated into the SSD backbone network to reconstruct its feature extraction structure. This module adaptively weights flame color and smoke texture along the channel dimension and highlights critical fire regions in the spatial dimension, substantially improving the model's perception of key fire features. Experimental results demonstrate that the CBAM-SSD model is lightweight and suitable for real-time detection, achieving a mAP@0.5 of 97.55% for flames and smoke, a 1.53% improvement over the baseline SSD. Specifically, the AP50 for flame detection reaches 96.61%, a 3.01% increase compared to the baseline, with a recall of 96.40%; while the AP50 for smoke detection reaches 98.49%, with a recall of 98.80%. These results indicate that the improved model delivers higher detection accuracy and lower false and missed detection rates, offering an efficient, convenient, and accurate solution for forest fire detection.