Small traffic sign recognition based on MND-YOLOv5

Traffic sign recognition plays a key role in the field of assisted driving and autonomous driving, helping drivers and autonomous vehicles accurately capture important road information. At present, in the process of traffic sign recognition, the recognition of small traffic signs is still challenging. In order to further improve the performance of the small traffic sign recognition algorithm, the MND-YOLOv5 algorithm is proposed. Firstly, the value of the detection model is enhanced through the K-Means re-concentration anchor value. And replace the model's main network part to ODCONV to enhance network feature extraction capabilities; Secondly, for the neck of the model, the cross-layer cross-connection is added, and the BIFPN_Concat is introduced to reconstruct the Concat connection to supplement the semantic information of the feature map in space and enhance the detection of small targets; At last, NWD is introduced into the detection head to replace the CIOU_ loss function to solve the sensitivity of the IOU to small target position deviations. The experimental results on the dataset TT100K show that the accuracy P of the MND-YOLOv5 algorithm is 87%, the recall rate is 79.8%, and the average accuracy (mAP@0.5) is 85.3%, which is 4.7%, 7.7% and 5.9% higher than that of YOLOv5s, respectively, and has better detection performance.