计算机工程与应用2025，Vol.61Issue(15)：93-110,18.DOI:10.3778/j.issn.1002-8331.2501-0158
结合目标特征增强与语义-位置路径聚合的水下目标检测
Underwater Object Detection Combining Target Feature Enhancement and Semantic-Location Path Aggregation
摘要
Abstract
To address the issues of missed and false detections caused by poor underwater image quality,multi-scale tar-gets,and severe occlusion,a novel underwater object detection(UOD)model is proposed.Based on the RT-DETR frame-work,the proposed UOD model introduces a multi-scale injection for edge features module(MSI-Edge)to inject edge information into the deep network,enhancing the model's perception of small objects.Additionally,a global-local feature enhancement module(GLF-Enhance)is proposed to replace the traditional multi-head self-attention mechanism in the encoder,improving the learning of global and local object information while accelerating inference.Furthermore,a new semantic-location path aggregation network(SL-PAN)is designed to address the degradation of information transmission during multi-scale feature fusion.SL-PAN utilizes high-level features as weights to guide semantic information learning in low-level features and low-level features as weights to guide positional information learning in high-level features.Experiments on public underwater datasets demonstrate that the proposed model outperforms the baseline model RT-DETR(with ResNet50 as the backbone),achieving approximately 3.2,3.0,and 2.7 percentage points improvements in AP,AP50,and AP75 metrics on the URPC dataset,and 2.9,2.7,and 3.0 percentage points improvements on the DUO dataset.The pro-posed method also effectively reduces false positive and missed detection rates.Ablation studies validate the effectiveness of each module.Compared to mainstream object detectors and the latest underwater object detection methods,the pro-posed model achieves competitive overall performance.关键词
水下目标检测/语义-位置路径聚合网络/边缘特征多尺度注入/RT-DETR模型/全局-局部特征增强Key words
underwater object detection/semantic-location path aggregation network/multi-scale injection for edge features/RT-DETR model/global-local feature enhance分类
信息技术与安全科学引用本文复制引用
宋巍,倪舟,梁纪辰,张明华,王建..结合目标特征增强与语义-位置路径聚合的水下目标检测[J].计算机工程与应用,2025,61(15):93-110,18.基金项目
国家自然科学基金(61972240). （61972240）
