Offshore ship object detection is a very challenging task and has received widespread attention from scholars and experts. Detectors based on Convolutional Neural Networks (CNN) and attention mechanisms have made significant progress in offshore ship object detection. However, the problem of false detection in the detection process is caused by the apparent similarity and background interference of ship targets. In order to solve this problem, this paper proposes a detection head module for fine-grained appearance discrimination implemented with Faster RCNN. This module includes a category fine-grained branch and an efficient full-dimensional dynamic convolution localization branch. The category fine-grained branch mines and utilizes category fine-grained identification features through global feature modeling and flexible perception range. The efficient omni-dimensional dynamic convolution positioning branch distinguishes objects and backgrounds through the efficient and flexible perception of ship boundary information, thereby reducing false and missed detections. Through experimental verification on the offshore ship public dataset Seaships7000, the proposed algorithm reduces false detections and missed detections and improves detector performance.