Multi-Objective Detection of River and Lake Spaces Based on YOLOv11n
Ling Liu, Tianyue Sun, Xiaoying Guo, Zhenguang Yuan
TL;DR
This paper introduces an improved YOLO model for detecting pollutants and issues in river and lake environments with higher accuracy.
Contribution
The novel YOLOv11n-DDH model integrates DySnakeConv, DAttention, and HSFPN to enhance detection accuracy in complex aquatic environments.
Findings
The YOLOv11n-DDH model achieved 88.4% precision, 78.9% recall, and 83.9% mAP in detecting river and lake pollutants.
The model outperformed the original YOLOv11n by 2.5 percentage points in mAP@50 with improvements from each added component.
The system effectively identifies pollutants like underwater waste and illegal fishing, aiding intelligent water management.
Abstract
In response to the challenges of target recognition and misjudgment caused by varying target scales, diverse shapes, and interference such as lake surface reflections in river and lake scenarios, this paper proposes the YOLO v11n-DDH model for fast and detection of spatial targets in river and lake environments. The model builds upon YOLO v11n by introducing the Dynamic Snake Convolution (DySnakeConv) to enhance the ability to extract detailed features. It integrates the Deformable Attention Mechanism (DAttention) to strengthen key features and suppress noise, while combining the improved High-Level Screening Feature Pyramid Network (HSFPN) structure for multi-level feature fusion, thus improving the semantic representation of targets at different scales. Experiments on a self-constructed dataset show that the precision, recall, and mAP of the YOLO v11n-DDH model reached 88.4%, 78.9%,…
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Taxonomy
TopicsAdvanced Neural Network Applications · Flood Risk Assessment and Management · Oil Spill Detection and Mitigation