A lightweight DGC_YOLOv5 (you only look once v5) algorithm is proposed to solve the problems of poor detection capability of small targets,large size of the model,complex calculation,and difficult deployment on mobile devices for flame detection model.Firstly,the k-means calculation function is used to calculate the anchor size for this data set.Secondly,the convolutional block attention module (CBAM) is introduced to improve the detection ability of this algorithm to small target.Then the lightweight Ghost module is adopted to improve the C3 modules in backbone network.Finally,the depthwise separable convolution (DS_Conv) which uses simple linear calculation instead of complicated calculation is used to reduce model complexity and size.Experiments show that compared with the original YOLOv5 algorithm,the mean average precision (mAP) of the proposed algorithm can reach 94.4% on the test set,1.7% higher than the original algorithm.The average detection speed of the proposed algorithm can reach 71 FPS on the video test set,which can meet the requirements of real-time detection.Parameters and the floating-point operations (FLOPs) calculating amount are respectively reduced to 41.2% and 34.8% of the original algorithm,and the model size is reduced by 8.4 M,which facilitates the subsequent deployment on mobile devices.