To address the low efficiency and accuracy of smoke & fire detection due to the small size of target and the confusion of fire feature with actual scene in complex environment, a small scale smoke & fire target detection method based on improved YOLOv5 is proposed.First, a fourth detection layer is added to the third detection layer output in the original YOLOv5 model, so as to obtain a larger feature map for small target detection and strengthen the feature extraction capability of the network model.Second, to solve the easy missing detection of target in shielded scene, DIoU_Loss is used to replace the GIoU_Loss in calculating the regression loss function of the target frame.Finally, TensorRT is used to compress and accelerate the optimization of the model, and then deployed to the Jetson TX2 development board for accelerated inference experiments.In addition, more smoke & fire scene data are constructed by replication enhancement.Experimental results show that the proposed method has fast convergence speed and high accuracy for small scale smoke & fire detection, possessing the prospect for popularization and application.