  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.
