A transformer and large-kernel convolution-based detection model for Red Turpentine Beetle infestation in pine trees

Infestation by the Red Turpentine Beetle is one of the key factors threatening the ecological stability of pine forests, while traditional manual inspection methods are inefficient and easily influenced by subjective factors. UAV-based detection methods can significantly improve monitoring efficiency; however, small target scales, blurred details, and complex backgrounds in UAV imagery pose great challenges to detection algorithms. To address these issues, This paper proposes TLK-YOLO, a UAV-based method for detecting pine trees infested by the Red Turpentine Beetle, which integrates a Transformer mechanism with a large-kernel selection strategy. A Local Window Cross-Attention (LWCA) upsampling module is introduced to enhance small-object detail modeling by fusing high- and low-resolution information within local windows. A Dynamic Combined Large Selective Kernel (DCLSK) module adaptively adjusts the receptive field to balance global context and local details, improving small-object feature discriminability and robustness. Furthermore, a redesigned network architecture, TLK-NA, mitigates redundancy in shallow-to-deep feature propagation by reversing the information flow from deep to shallow layers, achieving a better trade-off between detection accuracy and computational efficiency. Experimental results demonstrate that TLK-YOLO exhibits remarkable performance in detecting Red Turpentine Beetle infestations from UAV imagery, achieving an mAP50 of 89.3%. Compared with the baseline model, TLK-YOLO attains comprehensive improvements in both detection precision and recall, with precision increases by 4.4%, recall precision by 9.6%, while mAP50 and mAP50-95 improve by 7.9% and 8.3%, respectively, while maintaining relatively low computational overhead. This network provides an efficient and reliable technical foundation for UAV-based forestry pest and disease monitoring.