文献类型: 外文期刊
作者: Li, Penglong 1 ; Zhang, Shengmao 1 ; Zheng, Hanfeng 1 ; Fan, Xiumei 1 ; Shi, Yonchuang 1 ; Wu, Zuli 1 ; Zhang, Heng 1 ;
作者机构: 1.Chinese Acad Fishery Sci, East China Sea Fisheries Res Inst, Key Lab Fisheries Remote Sensing, Minist Agr & Rural Affairs, Shanghai 200090, Peoples R China
2.Dalian Ocean Univ, Sch Nav & Naval Architecture, Dalian 116023, Peoples R China
3.Huzhou Univ, Sch Informat Engn, Huzhou 313000, Peoples R China
4.Laoshan Lab, Qingdao 266237, Peoples R China
关键词:
期刊名称:FISHES ( 影响因子:2.4; 五年影响因子:2.4 )
ISSN:
年卷期: 2025 年 10 卷 8 期
页码:
收录情况: SCI
摘要: This study addresses the challenges of manual measurement bias and low robustness in detecting small, occluded targets in complex marine environments during real-time onboard sorting of Portunus trituberculatus. We propose YOLO-DFAM, an enhanced YOLOv11n-based model that replaces the global average pooling in the Focal Modulation module with a spatial-channel dual-attention mechanism and incorporates the ASF-YOLO cross-scale fusion strategy to improve feature representation across varying target sizes. These enhancements significantly boost detection, achieving an mAP@50 of 98.0% and precision of 94.6%, outperforming RetinaNet-CSL and Rotated Faster R-CNN by up to 6.3% while maintaining real-time inference at 180.3 FPS with only 7.2 GFLOPs. Unlike prior static-scene approaches, our unified framework integrates attention-guided detection, scale-adaptive tracking, and lightweight weight estimation for dynamic marine conditions. A ByteTrack-based tracking module with dynamic scale calibration, EMA filtering, and optical flow compensation ensures stable multi-frame tracking. Additionally, a region-specific allometric weight estimation model (R2 = 0.9856) reduces dimensional errors by 85.7% and maintains prediction errors below 4.7% using only 12 spline-interpolated calibration sets. YOLO-DFAM provides an accurate, efficient solution for intelligent onboard fishery monitoring.
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