The invention belongs to the technical field of image processing, and discloses an enhanced full convolution instance semantic segmentation algorithm suitable for small target detection based on a full convolution instance semantic segmentation (FCIS) algorithm, which comprises the steps of shared feature map extraction, preselection frame extraction, generation of a position sensitive score map,classification and regression. In the extraction process of the shared feature map, a conv1 feature map, a conv3 feature map and a conv5 feature map are fused, so that high-semantic information and high-detail information are reserved in the shared feature map; In the pre-selection frame extraction process, a dual RPN algorithm is provided for the poor network extraction effect of the pre-selection frame, and the average recall rate of the algorithm is increased by 7% compared with that of the RPN algorithm. The mAP of the EFCIS algorithm is improved by 3.5% compared with the FCIS algorithm, and for a small-size target, the mAP of the EFCIS algorithm is improved by 2.9% compared with the FCIS algorithm. Experiments show that the small target grabbing capacity can be improved very easily.