Aiming at the problem of poor recognition precision and serious missed detection of small traffic signs in bad weather such as haze， rain and snow， a traffic sign recognition model in haze weather based on YOLOv5 （You Only Look Once version 5） was proposed. Firstly， the structure of YOLOv5 was optimized. By using contrary thinking， the problem of small object recognition difficulty was solved by reducing the depth of feature pyramid and limiting the maximum down sampling multiple. By adjusting the depth of residual module， the repeated overlapping of background features was suppressed. Secondly， the mechanisms such as data augmentation， K-means anchor and Global Non-Maximum Suppression （GNMS） were introduced into the model. Finally， the detection ability of the improved YOLOv5 facing the bad weather was verified on the Chinese traffic sign dataset TT100K， and the study on traffic sign recognition in the haze weather with the most obvious precision decline was focused on. Experimental results show that the F1-score， mean Average Precision @0.5 （mAP@0.5）， mean Average Precision @0.5：0.95 （mAP@0.5：0.95） of the improved YOLOv5 model reach 0.921 50， 95.3% and 75.2%， respectively. The proposed model can maintain high-precision recognition of traffic sign in bad weather， and has Frames Per Second （FPS） up to 50， meeting the requirement of real-time detection.