An improved YOLOv5s for visual detection of smoke and fire in early-stage road tunnel fires was proposed to solve the problem of smoke and fire confusion and the requirement for real-time detection. The convolutional block attention module (CBAM) was introduced into YOLOv5s to improve the accuracy of detecting smoke with obscure contour features and initial tunnel flame with crucial features. The Focus module in the backbone network was replaced, the number of convolutional layers in BottleneckCSP was reduced, and the efficiency of the smoke and flame feature extraction network was improved. The CIoU was used to replace the original GIoU loss function to accelerate the convergence rate of the model. A data set containing 10 000 images of tunnel smoke and flame was used as the training sample. YOLOv5s and improved YOLOv5s-PRO were used for comparative test analysis. The model was validated by using the video data of the Zhenwu Mountain tunnel fire that occurred on March 6, 2021, in Chongqing, China. The experimental results showed that the detection accuracy of the algorithm reached up to 91.53%, which was 3.21% higher than YOLOv5s, and the detection speed reached 6.12 ms, which was 0.42 ms better than YOLOv5s. The YOLOv5s-PRO has higher detection accuracy and a faster rate, which can be applied to smoke and flame detection of actual road tunnel.