UAV-based object recognition dataset for coastal sewage outfalls

doi:10.13474/j.cnki.11-2246.2025.0818

Abstract

Abstract: The identification of coastal sewage outfalls is a crucial aspect of marine supervision,providing essential safeguards for the ecological and resource security of marine areas.Addressing the current challenges of insufficient specialized datasets and the lack of precision in target recognition algorithms for coastal sewage outfall detection using unmanned aerial vehicle (UAV)imagery,this study constructs a high-quality dataset of coastal sewage outfalls and proposes an enhanced detection method based on the improved YOLOv8n model.Initially,focusing on the coastal region of Yangjiang city,Guangdong province,the study employs UAVs to capture images at various altitudes,establishing a comprehensive dataset that encompasses diverse characteristics of sewage outfalls.Subsequently,the YOLOv8n model is augmented with the SimAM parameter-free attention mechanism to refine feature extraction and fusion,alongside the integration of NWD and CIoU loss functions to address issues of boundary ambiguity and target overlap.Experimental results demonstrate that the enhanced model surpasses the original in terms of precision,recall rate,and mAP,achieving an mAP of 98.27%.This research offers an intelligent solution for monitoring coastal sewage outfalls,contributing technological support for marine supervision and pollution control.

Key words: coastal, target detection, YOLOv8n, SimAM attention mechanism, UAV technology

CLC Number:

P237
P229

YIN Junjie, GUAN Daiwanjing, LI Hao, ZHANG Xiaoyang, MA Yujie, XING Hanfa. UAV-based object recognition dataset for coastal sewage outfalls[J]. Bulletin of Surveying and Mapping, 2025, 0(8): 112-117.

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