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TECI-YOLO: An Efficient, Lightweight Model for Detecting Small Floating Objects on Water Surfaces
Summary
Despite its title referencing floating object detection on water, this paper studies a machine learning model (TECI-YOLO) for detecting small objects on water surfaces using computer vision — not microplastic pollution. It examines improvements in detection accuracy and computational efficiency for real-time marine monitoring and is not directly relevant to microplastics research.
Timely detection of water-surface floating objects is critical for marine ecological protection, yet illumination variation, wave interference, and dense small-target distributions pose persistent challenges of low accuracy, high false-alarm rates, and excessive computational cost. This study proposes TECI-YOLO, a lightweight detection framework built upon YOLOv11s with four targeted improvements. The Tiny module adds a P2 layer while removing P5, preserving high-resolution spatial detail for small-target representation. The CEM–CFE module combines Channel-Enhanced MBConv and Channel-Fused Enhancer to strengthen feature discriminability and semantic robustness. The E_Head integrates coordinate attention, grouped convolution, and task-decoupled branches to reduce redundancy and improve localization. Inner-MPDIoU replaces standard MPDIoU with adaptive scaling and auxiliary bounding boxes for refined small-target geometric modeling. On FloW-IMG, TECI-YOLO improves Precision, Recall, mAP@0.5, and mAP@0.5:0.95 by 2.4%, 3.8%, 3.3%, and 0.6% over YOLOv11s; on IWHR_AI_Label_Floater_V1, gains reach 1.3%, 1.4%, and 0.7%, respectively. Parameters are reduced by ~26% with 3.8% fewer FLOPs, demonstrating a compelling accuracy–efficiency tradeoff for real-time water-surface monitoring.
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