YOLO-ALS: Dynamic Convolution With Adaptive Local Context for Remote Sensing Target Detection

Remote sensing image target detection plays a pivotal role in Earth observation, offering substantial value for applications such as urban planning and environmental monitoring. Due to the significant scale variations among targets, complex backgrounds with dense small object distributions, and strong intertarget scene correlations, existing target detection methods usually fail to effectively model target relationships and contextual information for remote sensing imagery. To address these limitations, we proposed YOLO-ALS, a novel remote sensing target detection network that integrates adaptive local scene context. The proposed framework introduces three key points. First, a full-dimensional dynamic convolution reconstruction C2f module enhances target feature representation by overcoming local context extraction limitations and target co-occurrence prior deficiencies. Second, an adaptive local scene context module (ALSCM) dynamically integrates multiscale receptive field features through spatial attention, enabling background window adaptive selection and cross-scale feature alignment. Finally, a co-occurrence matrix-integrated classification auxiliary module mines target association rules through data-driven learning, correcting classification probabilities in low-confidence areas by combining high-confidence areas’ co-occurrence information with an optimal threshold, which can significantly reduce missed detection rates. Comprehensive experiments on multiple public remote sensing datasets demonstrate the superiority of the proposed method through extensive ablation studies and comparative analyses. The proposed method has achieved state-of-the-art performance while addressing the unique challenges of remote sensing target detection.