An image segmentation method, comprising: acquiring a target image to be processed; acquiring image feature data of the target image; inputting the image feature data into a pre-trained target network for image segmentation to obtain an output; wherein the target network is a fully convolutional network comprising a hybrid context network structure, and the hybrid context network structure is configured to extract a plurality of reference features at a predetermined scale and fuse them into a target feature that matches a scale of a target object in a segmented image; and wherein the target network is trained with sample images containing target objects at different scales, obtaining an image segmentation result for the target image based on the output, the method being characterized in that the hybrid context network structure comprises at least one hybrid context component; and each hybrid context component comprises: an atrous convolution branch, a non-atrous convolution branch, a channel concatenating layer, and a non-atrous convolution layer, wherein the atrous convolution branch and the non-atrous convolution branch respectively convolve the input of this hybrid context component; the channel concatenating layer concatenates a convolution result from the atrous convolution branch and a convolution result from the non-atrous convolution branch; and the non-atrous convolution layer convolves a result from the channel concatenating layer to produce a convolution result as an output of this hybrid context component. acquiring a target image to be processed; acquiring image feature data of the target image; inputting the image feature data into a pre-trained target network for image segmentation to obtain an output; wherein the target network is a fully convolutional network comprising a hybrid context network structure, and the hybrid context network structure is configured to extract a plurality of reference features at a predetermined scale and fuse them into a target feature that matches a scale of a target object in a segmented image; and wherein the target network is trained with sample images containing target objects at different scales, obtaining an image segmentation result for the target image based on the output, the method being characterized in that the hybrid context network structure comprises at least one hybrid context component; and each hybrid context component comprises: an atrous convolution branch, a non-atrous convolution branch, a channel concatenating layer, and a non-atrous convolution layer, wherein the atrous convolution branch and the non-atrous convolution branch respectively convolve the input of this hybrid context component; the channel concatenating layer concatenates a convolution result from the atrous convolution branch and a convolution result from the non-atrous convolution branch; and the non-atrous convolution layer convolves a result from the channel concatenating layer to produce a convolution result as an output of this hybrid context component. obtaining an image segmentation result for the target image based on the output, the method being characterized in that the hybrid context network structure comprises at least one hybrid context component; and each hybrid context component comprises: an atrous convolution branch, a non-atrous convolution branch, a channel concatenating layer, and a non-atrous convolution layer, wherein the atrous convolution branch and the non-atrous convolution branch respectively convolve the input of this hybrid context component; the channel concatenating layer concatenates a convolution result from the atrous convolution branch and a convolution result from the non-atrous convolution branch; and the non-atrous convolution layer convolves a result from the channel concatenating layer to produce a convolution result as an output of this hybrid context component.