Deep learning‐based super‐resolution reconstruction and improved YOLOv9 for efficient benthos detection: a case study at Lake Hamana, Japan

IF 4.3 2区环境科学与生态学 Q1 ECOLOGY

Remote Sensing in Ecology and Conservation Pub Date : 2026-03-14 DOI:10.1002/rse2.70066

Fan Zhao, Bangzhang Ma, Dianhan Xi, Jiaqi Wang, Yijia Chen, Yongying Liu, Xinlei Shao, Mowen Zhang, Guocheng Zhang, Jundong Chen, Katsunori Mizuno

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Abstract

The development of remote sensing and object detection technologies has advanced benthos surveys. However, challenges remain in accuracy and cost‐efficiency due to environmental interference. A practical method combining drone‐based image acquisition and deep learning techniques for benthos monitoring is presented. Field experiments objecting hermit crabs were conducted at Lake Hamana using drones at altitudes of 2 m, 5 m and 10 m. Super‐resolution reconstruction (SRR) was applied to enhance image quality, followed by small‐object detection using the self‐built V9‐BENTHOS. With a magnification factor × 4, Residual Dense Network (RDN) achieved optimal SRR performance (PSNR: 38.15 dB, SSIM: 88.51%) and V9‐BENTHOS reached a mean average precision of 95.5%. The effects of SRR algorithms and magnification factors on hermit crab detection were discussed. This case study provides a new approach to support benthos ecological monitoring.

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基于深度学习的超分辨率重建和改进的YOLOv9高效底栖生物检测：以日本滨湖为例

遥感和目标探测技术的发展推动了底栖生物调查的发展。然而，由于环境干扰，在准确性和成本效率方面仍然存在挑战。提出了一种结合无人机图像采集和深度学习技术的底栖生物监测实用方法。在哈马纳湖，利用无人机在海拔2米、5米和10米的高度进行了寄居蟹的野外实验。超分辨率重建（SRR）用于提高图像质量，然后使用自制的V9‐BENTHOS进行小目标检测。在放大倍数为4倍的情况下，残差密集网络（RDN）获得了最佳的SRR性能（PSNR: 38.15 dB, SSIM: 88.51%）， V9‐BENTHOS的平均精度达到95.5%。讨论了SRR算法和放大因子对寄居蟹检测的影响。本研究为支持底栖生物生态监测提供了新的途径。

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来源期刊

Remote Sensing in Ecology and Conservation Earth and Planetary Sciences-Computers in Earth Sciences

CiteScore

9.80

自引率

5.50%

发文量

审稿时长

18 weeks

期刊介绍： emote Sensing in Ecology and Conservation provides a forum for rapid, peer-reviewed publication of novel, multidisciplinary research at the interface between remote sensing science and ecology and conservation. The journal prioritizes findings that advance the scientific basis of ecology and conservation, promoting the development of remote-sensing based methods relevant to the management of land use and biological systems at all levels, from populations and species to ecosystems and biomes. The journal defines remote sensing in its broadest sense, including data acquisition by hand-held and fixed ground-based sensors, such as camera traps and acoustic recorders, and sensors on airplanes and satellites. The intended journal’s audience includes ecologists, conservation scientists, policy makers, managers of terrestrial and aquatic systems, remote sensing scientists, and students. Remote Sensing in Ecology and Conservation is a fully open access journal from Wiley and the Zoological Society of London. Remote sensing has enormous potential as to provide information on the state of, and pressures on, biological diversity and ecosystem services, at multiple spatial and temporal scales. This new publication provides a forum for multidisciplinary research in remote sensing science, ecological research and conservation science.