Farm Vehicle Following Distance Estimation Using Deep Learning and Monocular Camera Images.

This paper presents a comprehensive solution for distance estimation of the following vehicle solely based on visual data from a low-resolution monocular camera. To this end, a pair of vehicles were instrumented with real-time kinematic (RTK) GPS, and the lead vehicle was equipped with custom devices that recorded video of the following vehicle. Forty trials were recorded with a sedan as the following vehicle, and then the procedure was repeated with a pickup truck in the following position. Vehicle detection was then conducted by employing a deep-learning-based framework on the video footage. Finally, the outputs of the detection were used for following distance estimation. In this study, three main methods for distance estimation were considered and compared: linear regression model, pinhole model, and artificial neural network (ANN). RTK GPS was used as the ground truth for distance estimation. The output of this study can contribute to the methodological base for further understanding of driver following behavior with a long-term goal of reducing rear-end collisions.

Prevalence, Risk Factors, and Outcomes of Platelet Transfusion Refractoriness in Critically Ill Patients: A Retrospective Cohort Study.

BACKGROUND: Refractoriness to platelet transfusion is an understudied phenomenon in critically ill patients. Our objective was to evaluate the prevalence, risk factors, and clinical outcomes of platelet refractoriness among patients in a tertiary-care intensive care unit (ICU). METHODS: A retrospective cohort study included all patients (age >14 years) who were admitted to a tertiary-care medical-surgical ICU between 2011 and 2016 and received ≥2 platelet transfusions during their ICU stay. We calculated platelet increment (PI) and corrected count increment (CCI). RESULTS: A total of 267 patients were enrolled in the study, collectively receiving 1357 transfusions with a median of 4.0 (interquartile range: 2.0, 6.0) transfusions per patient. The median pretransfusion platelet count was 31000.0 × 106/L (interquartile range: 16000.0, 50000.0). The median PI was 6000 × 106/L. The prevalence of platelet transfusion refractoriness was 54.8% based on PI < 10000 × 106/L and 57.0% based on CCI <5000. Patients admitted under hepatology/liver transplant had the highest rates of platelet refractoriness (69.6%), while those under general surgery had the lowest rate (43.2%). Younger age, nontrauma admission, and larger spleen size were associated with platelet refractoriness. Finally, refractoriness was associated with increased length of stay in the ICU (p = 0.02), but not with mortality. CONCLUSIONS: Platelet transfusion refractoriness was highly (>50%) prevalent in ICU patients. However, it was not associated with increased mortality.

Deep Learning-Based Object Detection for Unmanned Aerial Systems (UASs)-Based Inspections of Construction Stormwater Practices.

Construction activities typically create large amounts of ground disturbance, which can lead to increased rates of soil erosion. Construction stormwater practices are used on active jobsites to protect downstream waterbodies from offsite sediment transport. Federal and state regulations require routine pollution prevention inspections to ensure that temporary stormwater practices are in place and performing as intended. This study addresses the existing challenges and limitations in the construction stormwater inspections and presents a unique approach for performing unmanned aerial system (UAS)-based inspections. Deep learning-based object detection principles were applied to identify and locate practices installed on active construction sites. The system integrates a post-processing stage by clustering results. The developed framework consists of data preparation with aerial inspections, model training, validation of the model, and testing for accuracy. The developed model was created from 800 aerial images and was used to detect four different types of construction stormwater practices at 100% accuracy on the Mean Average Precision (MAP) with minimal false positive detections. Results indicate that object detection could be implemented on UAS-acquired imagery as a novel approach to construction stormwater inspections and provide accurate results for site plan comparisons by rapidly detecting the quantity and location of field-installed stormwater practices.