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With deep-learning-based object detection methods reaching industrial-level performance, underwater object detection has emerged as a significant application. However, it is often challenged by dense small instances and image blurring due to the water medium. In this paper, a Multi-Scale Feature Enhancement(MSFE) method is presented to address the challenges triggered by water bodies. In brief, MSFE attempts to achieve dual multi-scale information integration through the internal structural design of the basic C2F module in the Backbone network and the external global design of the feature pyramid network (FPN). For the internal multi-scale implementation, a LABNK module is constructed to address the vanishing or weakening phenomenon of fine-grained features during feature extraction. Specifically, it adopts a symmetrical structure to collaboratively capture two types of local receptive field information. Furthermore, to enhance the information integration ability between inter-layer features in FPN, a shallow feature branch is injected to supplement detailed features for the subsequent integration of multi-scale features. This operation is mainly supported by the fact that large-sized features from the shallow layer usually carry rich, fine-grained information. Taking the typical YOLOv8n as the benchmark model, extensive experimental comparisons on public underwater datasets (DUO and RUOD) demonstrated the effectiveness of the presented MSFE method. For example, taking the rigorous mAP (50:95) as an evaluation metric, it can achieve an accuracy improvement of about 2.8%. [ABSTRACT FROM AUTHOR]