Parameter Efficient Asymmetric Feature Pyramid for Early Wildfire Detection

This work addresses the need for high recall with low false alarms in early wildfire monitoring and presents AsymmetricFPN, an asymmetric feature pyramid centered on parameter efficiency. Within a RetinaNet framework, we replace Smooth L1 with CIoU to stabilize small object localization and perform lightweight post-fusion refinement at key sites where multiscale context is already integrated. We construct a composite dataset with perceptual hash deduplication and evaluate all models under a unified protocol. Results show that AsymmetricFPN achieves mAP@0.5 of 85.5% and recall of 81.2%, reaches mAP@[0.5:0.95] of 44.0%, attains the highest parameter efficiency (η = 2.34), and delivers 26.10 FPS end-to-end. In addition, the localization-aware efficiency, defined as η@[0.5:0.95] = mAP@[0.5:0.95]/Params(M), reaches ≈ 1.21 and is the highest under the unified protocol. Compared with representative one stage baselines under identical settings, it provides comparable core detection capability with fewer parameters and fewer false positives in confounding backgrounds such as water glare and sunset. We conclude that task-oriented asymmetric architecture with lightweight post-fusion refinement offers a reusable route to reconcile accuracy, efficiency, and deployment cost for practical wildfire detection.