DROMAL-Net: A Forest Larch Casebearer Detection Model Focusing on Global Feature Enhancement and Positional Dynamic Clustering

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Larch casebearer (Coleophora laricella) poses a serious threat to forest ecological security, and remote sensing object detection is essential for early warning and targeted intervention. However, existing detectors are hindered by insufficient feature extraction, limited self-attention inductive bias, and weak spatial localization, which collectively constrain their practical deployment. To address these issues, we propose DROMAL-Net, which integrates global feature enhancement and positional dynamic clustering for forest pest detection. Built upon the YOLOv11n baseline, our model incorporates three key innovations. First, we design the MALAPSA module by embedding a magnitude-aware linear attention (MALA) mechanism into the feature extraction pipeline, achieving linear computational complexity while preserving global contextual modeling. Second, to address the inductive bias deficiency of C2PSA and better preserve spatial topology, we introduce C3kDR (DCCC3k), a positional dynamic clustering module that combines dynamic clustering convolution with complex-domain positional compensation to enhance multi-scale localization accuracy. Third, we integrate 2D Rotary Position Embedding (2D-RoPE) to further mitigate spatial ambiguity in dynamic clustering.Extensive experiments on the public SLFPD dataset validate the effectiveness of each component. Under the standard random partition, DROMAL-Net achieves 77.98% mAP@0.5, outperforming YOLOv11n by 2.46 percentage points. Furthermore, under a strict leave-one-block-out protocol to prevent spatial leakage, DROMAL-Net consistently outperforms YOLOv11n across all five held-out blocks, achieving a macro-average mAP@0.5 of 69.12% vs. 65.94%, corresponding to a 3.18 percentage point improvement, which confirms its strong generalization capability to unseen geographic regions. Results across five random seeds yield consistently low standard deviations, confirming the stability of these gains. We further evaluate generalization on the TreeFinder dataset, which encompasses diverse tree species and complex canopy structures, where DROMAL-Net maintains robust performance, underscoring the effectiveness of MALAPSA and C3kDR for global feature enhancement and accurate localization in cross-regional applications.