Search

Rice, being an important global food source, is susceptible to diseases during its growth, resulting in a negative impact on its yield. Existing models for rice disease detection have limitations in recognizing small-sized and irregularly shaped disease types. To address this issue, we propose a new model called YOLOv8_Rice, specifically designed for rice leaf disease detection based on the YOLOv8n object detection model. Firstly, we conducted experimental research to investigate the influence of various common attention mechanisms on the performance of YOLOv8. The aim was to optimize the model's ability to extract features from different types of targets. Secondly, we enhanced the model's adaptability to target deformation and spatial changes by incorporating deformable convolutions to improve the C2f module structure in the YOLOv8 model. Furthermore, we replaced the network structure of YOLOv8 with a weighted bidirectional feature pyramid network to achieve weighted feature fusion, aiming to improve model performance and reduce computational complexity. Finally, we replaced the IOU loss function design in the YOLOv8 model with Wise IOU to provide more accurate evaluation results. In comparison to YOLOv8n, our YOLOv8_Rice model achieved an average precision increase of 15.8% and an mAP@0.5 improvement of 18.7% while reducing GFLOPs by 24.7% during testing on the rice disease dataset. These results indicate that YOLOv8_Rice has significant potential for global rice disease detection applications. [ABSTRACT FROM AUTHOR]

Currently, the existing object detection methods have many limitations in detecting defects on the base surface of metal sensors, such as a high rate of false detection and missed detection. Therefore, we proposed an improved algorithm based on You Look Only Once (YOLO) v5s aiming to solve the problem. Firstly, the C3 module was poor at detecting small defects. To enrich the gradient flow information and improve the detection accuracy of defects, the C2f module was used to replace part of the C3 module in the neck of the YOLO v5s. Then, an improved attention mechanism named Dilated Global Attention Mechanism was proposed to make the network focus more on the important information features. The dilated convolution was integrated into the spatial attention mechanism to enhance the receptive field of the model, reduce the model size and improve the detection performance of small defects. Finally, we proposed a novel localization loss function named Intersection over Union (IoU) with Normalized Wasserstein Distance, which not only alleviated the issue of Complete IoU loss based metrics being sensitive to the location deviations of small defects but also adjusted to diverse datasets. Results from ablation experiments demonstrated that the improved YOLO v5s algorithm enhanced the detection of the mean Average Precision by 5.3% and the Precision rate (P) by 7% compared with the original algorithm. [ABSTRACT FROM AUTHOR]

Many studies have reported various minimally invasive techniques for continuous-flow left ventricular assist device implantation. There is no consensus on whether minimally invasive techniques can bring more benefits for patients compared with the conventional technique, due to the limited number of patients and diverse results in current studies. Our meta-analysis mainly discussed the comparison of minimally invasive and conventional techniques. We searched controlled trials from PubMed, Cochrane Library, and Embase databases until Dec 11, 2020. Perioperative and postoperative outcomes were analyzed among 10 included studies. The protocol has been registered with PROSPERO (CRD42020221532). There were no statistical differences in the 30-day mortality (OR 0.57; 95% CI 0.29 to 1.14), 6-month mortality (OR 0.66; 95% CI 0.41 to 1.05), neurological dysfunction (OR 1.10; 95% CI 0.69 to 1.76), major infection (OR 0.68; 95% CI 0.36 to 1.28), and pump thrombus (OR 1.49; 95% CI 0.63 to 3.52) among the cohorts. Minimally invasive techniques were associated with lower incidences of major bleeding (OR 0.39; 95% CI 0.22 to 0.68), severe right heart failure (OR 0.43; 95% CI 0.23 to 0.81), and less blood-product utilization (SMD −0.44). Sensitivity analysis suggested that minimally invasive techniques were associated with a lower incidence of respiratory failure (OR 0.50; 95% CI 0.26 to 0.96) and shorter mechanical ventilation time (SMD −0.53). Subgroup analysis demonstrated that patients, implanted with a centrifugal pump by minimally invasive techniques, were associated with a shorter length of intensive care unit (ICU) stay (SMD −0.27) and hospital stay (SMD −0.42), and less blood-product utilization (SMD −0.26). In conclusion, minimally invasive techniques can reduce the risks of major bleeding, severe right heart failure, and blood-product utilization, as well as have positive impacts on reducing mechanical ventilation time and the risk of respiratory failure. Minimally invasive centrifugal pump implantation can reduce the length of ICU and hospital stay. [ABSTRACT FROM AUTHOR]