Remote sensing imagery presents unique challenges for object detection due to wide fields of view, complex backgrounds, and the dense distribution of small targets, often rendering traditional methods ineffective. To address these limitations, we introduce GSS-YOLO, a lightweight network tailored for remote sensing environments. Our architecture integrates a Spatial Information Aggregation (SIA) module within a Cross-Stage Partial Network (C3) to optimize both detection accuracy and processing efficiency. Furthermore, we incorporate Spatial Pyramid Dilated Convolution (SPD-Conv) to enhance adaptability to low-resolution inputs, and embed a Global Context-Aware Module (GCAM) prior to the detection head to refine multi-scale feature representation. Evaluations on the USOD, VisDrone2019 and DIOR datasets demonstrate that GSS-YOLO achieves superior precision, recall, and robustness across both color and grayscale imagery, all while maintaining a lightweight architecture. Validated by ablation studies, this approach provides an efficient and robust solution for small target detection in complex remote sensing scenarios.
Wu et al. (Thu,) studied this question.