针对自动驾驶路面图像中行人目标难以识别、检测精度低等问题,提出了一种基于改进YOLOv11的道路物体检测算法。研究首先针对YOLOv11在多尺度特征建模能力上的局限性,设计了一种融合频域卷积建模与空间–通道注意力机制的改进模块。在YOLOv11的C3k2结构基础上引入优化后的FDConv模块,构建出Bottleneck_FDConv、C3k_FDConv与C3k2_ FDConv子结构,有效增强了模型对小目标和复杂场景的特征提取能力。同时,对YOLOv11原有的损失函数进行了优化：一方面引入Focal Loss以缓解正负样本不平衡问题,另一方面增加小目标加权机制,从而提升模型对行人等小目标的关注度。实验在公开数据集KITTI上进行验证,结果表明改进后的YOLOv11在行人检测指标上达到了74.43%,较原始YOLOv11提高了3.1%,改进后的 YOLOv11算法在mAP50这一指标上达到了84.87%,与原始的YOLOv11相比,提高了0.85%,显著提升了模型的检测精度和鲁棒性,具有较高的应用价值。

To address issues such as difficult pedestrian target recognition, and low detection accuracy in road images for autonomous driving, this paper proposes a road object detection algorithm based on the improved YOLOv11. First, aiming at the limitation of YOLOv11 in multi-scale feature modeling capability, an improved module integrating frequency-domain convolution modeling and spatial-channel attention mechanism is designed. On the basis of the C3k2 structure of YOLOv11, the optimized FDConv module is introduced to construct Bottleneck_FDConv, C3k_FDConv, and C3k2_FDConv sub-structures, which effectively enhance the model's feature extraction ability for small targets and complex scenes. Meanwhile, this paper optimizes the original loss function of YOLOv11: on one hand, Focal Loss is introduced to alleviate the problem of imbalanced positive and negative samples; on the other hand, a small target weighting mechanism is added to improve the model's attention to small targets such as pedestrians. Experiments are conducted on the public KITTI dataset for verification. The results show that the improved YOLOv11 achieves 74.43% in the pedestrian detection metric, which is 3.1% higher than the original YOLOv11. In terms of the mAP50 metric, the improved YOLOv11 algorithm reaches 84.87%, which is 0.85% higher than the original YOLOv11. These results demonstrate that the improved algorithm significantly enhances the model's detection accuracy and robustness, and has high application value.