Real-time and accurate detection of flames, smoke, and electric arcs is an important prerequisite for the safe production in factories. When dealing with complex features of flames, smoke, and arc targets, traditional detection algorithms suffer from insufficient accuracy, high missed detection rates for small targets. We proposed a smoke and fire electrical detection model MS-YOLOv5 based on YOLOv5s. First, the model’s detection accuracy for small targets is improved by clustering the anchor frame sizes suitable for small target detection based on the K-mean++ clustering algorithm. Second, the C3-DySnake module was designed, and dynamic snake convolution was utilized to replace the normal convolution module in the C3 module of the backbone network, giving the backbone network the flexibility to adaptively focus on key features. Third, the multidimensional collaborative attention module was added to the end of the neck network, so that the model focused on the local area information in the feature fusion stage while obtaining a stronger feature map. Finally, the alpha-intersection over union (α-IoU) loss function was used to retain the high-quality prediction frames, and effectively improve the model convergence speed. The MS-YOLOv5 detection model established on a self-constructed smoke and fire electrical dataset was high in precision and effective in recall, with better mAP@0.5-value compared with the original YOLOv5.