Substantial natural environmental damage and economic losses are caused by fire. For this problem, automatic fire-smoke detection and identification are needed. Fire-smoke detection methods based on vision still suffer from significant challenges that fail to balance model complexity and accuracy. We propose an improved YOLOv3 fire-smoke detection and identification method to address these problems and include a fire and smoke dataset. The neck module (1) adds an attention mechanism to enhance the ability to extract features from pictures, and (2) uses an anchor-free mechanism in the anchor box mechanism to solve the problem of significant variances in smoke texture, shape, and color in real applications, and (3) uses a lightweight backbone to reduce the model complexity. The proposed dataset is based on VOC, which contains images of complex scenes and high diversity. The dataset includes pictures that (1) combine fire with smoke, (2) only have smoke or fire objects, and (3) contain a single cloud object. The experimental results demonstrate that the method achieves 50.8 AP, which outperforms the suboptimal method by 3.8. Moreover, the inference speed of our method is 13% faster on the GPU than the suboptimal method.