Abstract: The existing image segmentation methods have good results when used for coal mine underground images with good clarity. But when the methods are applied to coal mine underground images with complex environments, the obtained images are mostly blurry and the contour of the target object is not clear. The result affects the segmentation precision of the target object. In order to solve the above problems, a coal mine image instance segmentation method based on improved SOLOv2 is proposed. The method replaces the ResNet-50 network of the SOLOv2 model with the ResNeXt-18 network to simplify the network layers and improve the inference speed of the model. The method introduces the coordinate attention (CA) module to enhance the model's feature extraction capability, retain precise positional information, and improve the model's image segmentation precision. The method replaces the ReLU activation function with the ACON-C activation function. The features between neurons can be fully combined, enhancing the model's feature expression capability, and further improving the image segmentation precision of the model. The improved SOLOv2 model is deployed on an embedded platform for coal mine image segmentation experiments. Compared to the SOLOv2 model, the Mask AP (mask average precision) of the improved SOLOv2 model increases by 1.1%, the weight file of the model decreases by 83.2 MiB. The inference speed increases by 5.30 frames/s, reaching 26.10 frames/s. Both the precision and inference speed of coal mine image segmentation are improved to a certain extent.