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Deep learning-based super-resolution reconstruction and improved YOLOv9 for efficient benthos detection: a case study at Lake Hamana, Japan
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DOI：10.1002/rse2.70066.png)
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Abstract
En 中文
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.
Keywords:
Benthos Detection
Lake Hamana
Super-resolution reconstruction
YOLOv9
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