Architecture of AdaptFFN.

With the rapid development of ocean observation technology, underwater object detection has begun to occupy an essential position in the fields of aquaculture, environmental monitoring, marine science, etc. However, due to the problems unique to underwater images such as severe noise, blurred objects, and multi-scale, deep learning-based target detection algorithms lack sufficient capabilities to cope with these challenges. To address these issues, we improve DETR to make it well suited for underwater scenarios. First, a simple and effective learnable query recall mechanism is proposed to mitigate the effect of noise and can significantly improve the detection performance of the object. Second, for underwater small and irregular object detection, a lightweight adapter is designed to provide multi-scale features for the encoding and decoding stages. Third, the regression mechanism of the bounding box is optimized using the combination loss of smooth L1 and CIoU. Finally, we validate the designed network against other state-of-the-art methods on the RUOD dataset. The experimental results show that the proposed method is effective.</div

Effectiveness of each component of our model.

With the rapid development of ocean observation technology, underwater object detection has begun to occupy an essential position in the fields of aquaculture, environmental monitoring, marine science, etc. However, due to the problems unique to underwater images such as severe noise, blurred objects, and multi-scale, deep learning-based target detection algorithms lack sufficient capabilities to cope with these challenges. To address these issues, we improve DETR to make it well suited for underwater scenarios. First, a simple and effective learnable query recall mechanism is proposed to mitigate the effect of noise and can significantly improve the detection performance of the object. Second, for underwater small and irregular object detection, a lightweight adapter is designed to provide multi-scale features for the encoding and decoding stages. Third, the regression mechanism of the bounding box is optimized using the combination loss of smooth L1 and CIoU. Finally, we validate the designed network against other state-of-the-art methods on the RUOD dataset. The experimental results show that the proposed method is effective.</div

Results of different methods for underwater object detection.

Results of different methods for underwater object detection.</p

Analysis of small-scale objects on the DUO dataset.

Analysis of small-scale objects on the DUO dataset.</p

Different query decoding mechanisms.

(a) Basic, (b) Dense Query Recollection, (c) Learnable Query Recall.</p

Comparison between models on RUOD.

With the rapid development of ocean observation technology, underwater object detection has begun to occupy an essential position in the fields of aquaculture, environmental monitoring, marine science, etc. However, due to the problems unique to underwater images such as severe noise, blurred objects, and multi-scale, deep learning-based target detection algorithms lack sufficient capabilities to cope with these challenges. To address these issues, we improve DETR to make it well suited for underwater scenarios. First, a simple and effective learnable query recall mechanism is proposed to mitigate the effect of noise and can significantly improve the detection performance of the object. Second, for underwater small and irregular object detection, a lightweight adapter is designed to provide multi-scale features for the encoding and decoding stages. Third, the regression mechanism of the bounding box is optimized using the combination loss of smooth L1 and CIoU. Finally, we validate the designed network against other state-of-the-art methods on the RUOD dataset. The experimental results show that the proposed method is effective.</div

Architecture of network’s transformer.

With the rapid development of ocean observation technology, underwater object detection has begun to occupy an essential position in the fields of aquaculture, environmental monitoring, marine science, etc. However, due to the problems unique to underwater images such as severe noise, blurred objects, and multi-scale, deep learning-based target detection algorithms lack sufficient capabilities to cope with these challenges. To address these issues, we improve DETR to make it well suited for underwater scenarios. First, a simple and effective learnable query recall mechanism is proposed to mitigate the effect of noise and can significantly improve the detection performance of the object. Second, for underwater small and irregular object detection, a lightweight adapter is designed to provide multi-scale features for the encoding and decoding stages. Third, the regression mechanism of the bounding box is optimized using the combination loss of smooth L1 and CIoU. Finally, we validate the designed network against other state-of-the-art methods on the RUOD dataset. The experimental results show that the proposed method is effective.</div

Effectiveness of the middle dimension.

With the rapid development of ocean observation technology, underwater object detection has begun to occupy an essential position in the fields of aquaculture, environmental monitoring, marine science, etc. However, due to the problems unique to underwater images such as severe noise, blurred objects, and multi-scale, deep learning-based target detection algorithms lack sufficient capabilities to cope with these challenges. To address these issues, we improve DETR to make it well suited for underwater scenarios. First, a simple and effective learnable query recall mechanism is proposed to mitigate the effect of noise and can significantly improve the detection performance of the object. Second, for underwater small and irregular object detection, a lightweight adapter is designed to provide multi-scale features for the encoding and decoding stages. Third, the regression mechanism of the bounding box is optimized using the combination loss of smooth L1 and CIoU. Finally, we validate the designed network against other state-of-the-art methods on the RUOD dataset. The experimental results show that the proposed method is effective.</div

DataSheet_1_The evolution and expression of stomatal regulators in C3 and C4 crops: Implications on the divergent drought tolerance.docx

Drought stress is a major environmental hazard. Stomatal development is highly responsive to abiotic stress and has been used as a cellular marker for drought-tolerant crop selection. C3 and C4 crops have evolved into different photosynthetic systems and physiological responses to water deficits. The genome sequences of maize, sorghum, and sugarcane make it possible to explore the association of the stomatal response to drought stress with the evolution of the key stomatal regulators. In this study, phylogenic analysis, gene expression analysis and stomatal assay under drought stress were used to investigate the drought tolerance of C3 and C4 plants. Our data shows that C3 and C4 plants exhibit different drought responses at the cellular level. Drought represses the growth and stomatal development of C3 crops but has little effect on that of C4 plants. In addition, stomatal development is unresponsive to drought in drought-tolerant C3 crops but is repressed in drought-tolerant C4 plants. The different developmental responses to drought in C3 and C4 plants might be associated with the divergent expression of their SPEECHLESS genes. In particular, C4 crops have evolved to generate multiple SPEECHLESS homologs with different genetic structure and expression levels. Our research provides not only molecular evidence that supports the evolutionary history of C4 from C3 plants but also a possible molecular model that controls the cellular response to abiotic stress in C3 and C4 crops.</p

Widefield scanning imaging with optical super-resolution

<div><p>An economical, pollution-free microsphere-based widefield scanning imaging method is presented. This system is able to visualize the surface pattern of the sample through a transparent dielectric microsphere stuck onto a glass probe. The microsphere endows the system with super-resolution capability, while the field of view can easily be expanded by scanning and image stitching. The feasibilities and advantages of this method have been verified experimentally.</p></div