YOLO-DFAM-Based Onboard Intelligent Sorting System for Portunus trituberculatus

文献类型： 外文期刊

第一作者： Li, Penglong

作者： Li, Penglong;Zhang, Shengmao;Zheng, Hanfeng;Fan, Xiumei;Shi, Yonchuang;Wu, Zuli;Zhang, Heng;Li, Penglong;Zhang, Shengmao;Zhang, Shengmao

作者机构：

关键词： Portunus trituberculatus; YOLOv11; detection and tracking; ByteTrack

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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