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.
Zhao et al. (Sat,) studied this question.