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

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.