CONCLUSIONS: Innovatively, we consider stomatal detection as rotated object detection and provide an end-to-end, batch, rotated, real-time stomatal density and aperture size intelligent detection and identification system, RotatedeStomataNet. Stomata acts as a pathway for air and water vapor in the course of respiration, transpiration, and other gas metabolism, so the stomata phenotype is important for plant growth and development. Intelligent detection of high-throughput stoma is a key issue. Nevertheless, currently available methods usually suffer from detection errors or cumbersome operations when facing densely and unevenly arranged stomata. The proposed RotatedStomataNet innovatively regards stomata detection as rotated object detection, enabling an end-to-end, real-time, and intelligent phenotype analysis of stomata and apertures. The system is constructed based on the Arabidopsis and maize stomatal data sets acquired destructively, and the maize stomatal data set acquired in a non-destructive way, enabling the one-stop automatic collection of phenotypic, such as the location, density, length, and width of stomata and apertures without step-by-step operations. The accuracy of this system to acquire stomata and apertures has been well demonstrated in monocotyledon and dicotyledon, such as Arabidopsis, soybean, wheat, and maize. The experimental results that the prediction results of the method are consistent with those of manual labeling. The test sets, the system code, and their usage are also given ( https://github.com/AITAhenu/RotatedStomataNet ).

The damage symptoms of Cnaphalocrocis medinalis (C.medinalis) is an important evaluation index for pest prevention and control. However, due to various shapes, arbitrary-oriented directions and heavy overlaps of C.medinalis damage symptoms under complex field conditions, generic object detection methods based on horizontal bounding box cannot achieve satisfactory results. To address this problem, we develop a Cnaphalocrocis medinalis damage symptom rotated detection framework called CMRD-Net. It mainly consists of a Horizontal-to-Rotated region proposal network (H2R-RPN) and a Rotated-to-Rotated region convolutional neural network (R2R-RCNN). First, the H2R-RPN is utilized to extract rotated region proposals, combined with adaptive positive sample selection that solves the hard definition of positive samples caused by oriented instances. Second, the R2R-RCNN performs feature alignment based on rotated proposals, and exploits oriented-aligned features to detect the damage symptoms. The experimental results on our constructed dataset show that our proposed method outperforms those state-of-the-art rotated object detection algorithms achieving 73.7% average precision (AP). Additionally, the results demonstrate that our method is more suitable than horizontal detection methods for in-field survey of C.medinalis.

UNASSIGNED: Recent years have witnessed the advancement of deep learning vision technologies and applications in the medical industry. Intelligent devices for specific medication management could alleviate workload of medical staff by providing assistance services to identify drug specifications and locations.
UNASSIGNED: In this work, object detectors based on the you only look once (YOLO) algorithm are tailored for toxic and narcotic medication detection tasks in which there are always numerous of arbitrarily oriented small bottles. Specifically, we propose a flexible annotation process that defines a rotated bounding box with a degree ranging from 0° to 90° without worry about the long-short edges. Moreover, a mask-mapping-based non-maximum suppression method has been leveraged to accelerate the post-processing speed and achieve a feasible and efficient medication detector that identifies arbitrarily oriented bounding boxes.
UNASSIGNED: Extensive experiments have demonstrated that rotated YOLO detectors are highly suitable for identifying densely arranged drugs. Six thousand synthetic data and 523 hospital collected images have been taken for training of the network. The mean average precision of the proposed network reaches 0.811 with an inference time of less than 300 ms.
UNASSIGNED: This study provides an accurate and fast drug detection solution for the management of special medications. The proposed rotated YOLO detector outperforms its YOLO counterpart in terms of precision.