基于改进YOLOv5的行人目标检测算法研究
Research on Pedestrian Target Detection Algorithm Based on Improved YOLOv5

本文针对行人目标检测中的挑战，提出了一种基于改进YOLOv5的行人检测算法。该算法融合了Ghost模块和SE注意力机制，旨在提高特征提取能力的同时，保持模型的轻量性。在面对密集场景和遮挡问题时，改进的YOLOv5能有效提取重要特征并提升检测精度。通过对比实验和模拟分析，验证了该算法在提升检测性能的同时，仍能保持较低的计算复杂度和较高的实时性。实验结果表明，所提算法在行人检测任务中具有较好的表现，尤其是在低照度和复杂背景条件下。
Aiming at the challenges in pedestrian target detection, this paper proposes a pedestrian detection algorithm based on improved YOLOv5. The algorithm combines the Ghost module and SE attention mechanism to improve the feature extraction capability while maintaining the lightweight of the model. When faced with dense scenes and occlusion problems, the improved YOLOv5 can effectively extract important features and improve detection accuracy. Through comparative experiments and simulation analysis, it is verified that the algorithm can maintain low computational complexity and high real-time performance while improving detection performance. Experimental results show that the proposed algorithm has good performance in pedestrian detection tasks, especially under low illumination and complex background conditions.