针对钢筋绑扎机器人目标检测算法在识别绑扎点时存在准确度低和检测速度慢等问题，提出了一种基于YOLOv5改进的钢筋网绑扎点目标检测方法FNB-YOLOv5。首先，通过采集钢筋网交叉点制作数据集；然后，在YOLOv5网络中引入FasterNet轻量化主干网络，增强特征信息提取能力，降低网络复杂度；其次，在网络关键部位引入BiFormer注意力机制，提高特征提取的准确性；考虑到检测任务中小尺寸目标占多数，采用NWD损失函数归一化处理，优化检测绑扎点位置的精度；最后，设计一种改进的F-GFPN特征融合模块，通过添加跳跃连接和跨尺度连接，增强特征交互，提升计算性能。通过不同模型对比实验得出，该模型的精确率、召回率和平均精度均值分别为99.82%、99.10%和98.64%，比原模型分别增长了2.9%、1.53%和1.63%；FPS达到44.8 f/s，比原模型增加4.1 f/s，同时权重模型内存大小减小了2.93MB。实验结果表明，改进的FNB-YOLOv5模型在钢筋网绑扎点数据集上，具有更高的准确性和实时性，为建筑施工行业研发钢筋绑扎机器人提供了有力的技术支持。

Aiming at the low accuracy and slow detection speed of rebar binding points in the target detection algorithm of rebar binding robots, we proposed a method for steel mesh binding point detection named FNBYOLOv5, which is based on YOLOv5. Firstly, by collecting the intersections of rebar grids to create a dataset. Secondly, FasterNet lightweight backbone network is introduced into the YOLOv5 network to enhance feature extraction capability and reduce network complexity. Then, the BiFormer attention mechanism improves the accuracy of feature extraction. And considering that small-sized targets dominate the detection task, the NWD loss function is used for normalization to optimize the accuracy of detecting binding points. Finally, a redesigned FGFPN feature fusion module is devised to enhance feature interaction and improve computational performance by incorporating skip connections and cross-scale connections. Comparative experiments with different models show that the precision, recall, and mean average precision (mAP) of the model are 99.82%, 99.10%, and 98.64%, respectively, representing an increase of 2.9%, 1.53%, and 1.63% over the original model. The frames per second (FPS) reach 44.8 f/s, an increase of 4.1 f/s compared to the original model, while the size of the weight model memory is reduced by 2.93MB. Experimental results demonstrate that the improved FNB-YOLOv5 model achieves higher accuracy and real-time performance on the rebar binding point dataset, providing technical support for the development of rebar binding robots in the construction industry.