HFQI-YOLO: a lightweight and efficient underwater object detection algorithm

Abstract

Underwater object detection plays a crucial role in fisheries resource assessment and ecological environment protection. Current underwater object detection models are characterized by large parameter sizes and high computational costs, which hinder the simultaneous achievement of lightweight deployment and high detection accuracy for real-time and resource-limited underwater platforms. Therefore, this paper proposes a lightweight underwater object detection method named HFQI-YOLO based on YOLO11n. First, a High-level Screening Path Aggregation Network with the Dysample is designed to replace the traditional PANet, which effectively improves multi-scale feature fusion quality while significantly reducing computational resources through bidirectional feature screening and dynamic upsampling. Subsequently, the Feature Complementary Mapping module is embedded into the backbone to mitigate the mismatch between deep semantic representations and shallow spatial details via effective semantic spatial complementary interactions. Moreover, a quality-aware shared detection head is introduced to enhance detection reliability. This design simultaneously decreases model parameters and resolves the inconsistency between classification confidence and localization precision. Finally, Inner WIoU is employed as the loss function to refine bounding box regression and increase sensitivity to small object instances. Experimental results demonstrate that the proposed algorithm outperforms the YOLO11n baseline on the URPC2020 dataset, achieving only 1.5 M and 4.3 GFLOPs, which are reduced by 41.7% and 31.7% respectively, and an increase of 0.9 percentage points in mAP@0.5%–82.6%. Furthermore, the generalization ability and robustness of the algorithm are validated on the RUOD dataset, further demonstrating its superior performance.