Small object detection presents a significant challenge in computer vision and object detection. The performance of small object detectors is often compromised by a lack of pixels and less significant features. This issue stems from information misalignment caused by variations in feature scale and information loss during feature processing. In response to this challenge, this paper proposes a novel the Multi to Single Module (M2S), which enhances a specific layer through improving feature extraction and refining features. Specifically, M2S includes the proposed Cross-scale Aggregation Module (CAM) and explored Dual Relationship Module (DRM) to improve information extraction capabilities and feature refinement effects. Moreover, this paper enhances the accuracy of small object detection by utilizing M2S to generate an additional detection head. The effectiveness of the proposed method is evaluated on two datasets, VisDrone2021-DET and SeaDronesSeeV2. The experimental results demonstrate its improved performance compared with existing methods. Compared to the baseline model (YOLOv5s), M2S improves the accuracy by about 1.1\% on the VisDrone2021-DET testing dataset and 15.68\% on the SeaDronesSeeV2 validation set.

翻译：小目标检测是计算机视觉和目标检测领域中的一个重大挑战。小目标检测器的性能常因像素不足和特征显著性较低而受到影响。这一问题源于特征尺度变化导致的信息错配以及特征处理过程中的信息丢失。针对这一挑战，本文提出了一种新颖的多到单模块（Multi to Single Module, M2S），该模块通过改进特征提取和精细化特征来增强特定网络层。具体而言，M2S包含所提出的跨尺度聚合模块（Cross-scale Aggregation Module, CAM）和探索的双关系模块（Dual Relationship Module, DRM），以提升信息提取能力和特征细化效果。此外，本文利用M2S生成额外的检测头，提高了小目标检测的精度。在两个数据集VisDrone2021-DET和SeaDronesSeeV2上对所提方法的有效性进行了评估。实验结果表明，与现有方法相比，其性能有所提升。与基线模型（YOLOv5s）相比，M2S在VisDrone2021-DET测试集上的精度提升了约1.1%，在SeaDronesSeeV2验证集上的精度提升了15.68%。