In this paper, the limitations of YOLOv5s model on small target detection task are deeply studied and improved. The performance of the model is successfully enhanced by introducing GhostNet-based convolutional module, RepGFPN-based Neck module optimization, CA and Transformer's attention mechanism, and loss function improvement using NWD. The experimental results validate the positive impact of these improvement strategies on model precision, recall and mAP. In particular, the improved model shows significant superiority in dealing with complex backgrounds and tiny targets in real-world application tests. This study provides an effective optimization strategy for the YOLOv5s model on small target detection, and lays a solid foundation for future related research and applications.

翻译：本文深入研究并改进了YOLOv5s模型在小目标检测任务中的局限性。通过引入基于GhostNet的卷积模块、基于RepGFPN的Neck模块优化、CA与Transformer的注意力机制，以及利用NWD进行损失函数改进，成功提升了模型性能。实验结果验证了这些改进策略对模型精确率、召回率和mAP的积极影响。特别是在实际应用测试中，改进后的模型在处理复杂背景和微小目标方面展现出显著优势。本研究为YOLOv5s模型在小目标检测方面提供了有效的优化策略，并为未来的相关研究与应用奠定了坚实基础。