Traffic signs are important facilities to ensure traffic safety and smooth flow, but may be damaged due to many reasons, which poses a great safety hazard. Therefore, it is important to study a method to detect damaged traffic signs. Existing object detection techniques for damaged traffic signs are still absent. Since damaged traffic signs are closer in appearance to normal ones, it is difficult to capture the detailed local damage features of damaged traffic signs using traditional object detection methods. In this paper, we propose an improved object detection method based on YOLOv5s, namely MFL-YOLO (Mutual Feature Levels Loss enhanced YOLO). We designed a simple cross-level loss function so that each level of the model has its own role, which is beneficial for the model to be able to learn more diverse features and improve the fine granularity. The method can be applied as a plug-and-play module and it does not increase the structural complexity or the computational complexity while improving the accuracy. We also replaced the traditional convolution and CSP with the GSConv and VoVGSCSP in the neck of YOLOv5s to reduce the scale and computational complexity. Compared with YOLOv5s, our MFL-YOLO improves 4.3 and 5.1 in F1 scores and mAP, while reducing the FLOPs by 8.9%. The Grad-CAM heat map visualization shows that our model can better focus on the local details of the damaged traffic signs. In addition, we also conducted experiments on CCTSDB2021 and TT100K to further validate the generalization of our model.

我们提出了基于YOLOv5s的改进目标检测方法MFL-YOLO（互补特征级别损失增强YOLO），通过设计简单的跨级别损失函数，使得模型的每个级别都具有自己的作用，有助于学习更多多样化的特征并提高细粒度。相比YOLOv5s，我们的MFL-YOLO在F1得分和mAP上分别提高了4.3和5.1，同时减少了8.9%的FLOPs。Grad-CAM热图可视化显示我们的模型能更好地关注损坏交通标志的局部细节。此外，我们还对CCTSDB2021和TT100K进行了实验证明我们模型的泛化性。

MFL-YOLO: 损坏交通标志物的目标检测模型