Metro systems are vital to our daily lives, but they face safety or reliability challenges, such as criminal activities or infrastructure disruptions, respectively. Real-time threat detection and analysis are crucial to ensure their safety and reliability. Although many existing systems use Twitter to detect metro-related threats or events in real-time, they have limitations in event analysis and system maintenance. Specifically, they cannot analyze event development, or prioritize events from numerous tweets. Besides, their users are required to continuously monitor system notifications, use inefficient content retrieval methods, and perform detailed system maintenance. We addressed those issues by developing the MetroScope system, a real-time threat/event detection system applied to Washington D.C. metro system. MetroScope can automatically analyze event development, prioritize events based on urgency, send emergency notifications via emails, provide efficient content retrieval, and self-maintain the system. Our MetroScope system is now available at http://orion.nvc.cs.vt.edu:5000/, with a video (https://www.youtube.com/watch?v=vKIK9M60-J8) introducing its features and instructions. MetroScope is a significant advancement in enhancing the safety and reliability of metro systems.

Medical dispensing refers to the in-office preparation and delivery of prescription drugs, which is mostly dispensed by the units of blister packages. The objective of the study is to design an image-based blister package identification solution, which is capable of identifying a fetched drug based on a pair of the two opposite camera images of the hand-held drug. To this aim, this paper proposes a deep learning based Hand-held Blister Identification network (HBIN) to identify partially occluded blister packages present in arbitrary positions and orientation with possibly cluttered backgrounds. The proposed HBIN is a two-stage network that contains Blister cropping network (BCN) followed by RTT identification network (RIN). The BCN subnetwork, an image to image translation deep learning network, is to crop both side contours of the hand-held drug, before the pair of cropped contours can be juxtaposed as a fixed sized and fixed orientation RTT (rectified two-sides template) for final identification in the RIN sub-network. A blister package dataset containing a total of 30,394 images based on 230 types, typically found in hospital dispensing stations, have been collected and labeled. With extensive test, the accuracy of the primitive primitive HBIN attains an F-score of more than 94.33% for testing data from similar backgrounds and an F-score of 79.80% for dissimilar backgrounds. Although still a prototype, the preliminary results show the feasibility of identifying blister packages during retrieval process without resorting to bar codes nor RFID tags.

Precise identification of blister packages carries utmost importance at dispensing stations, where numerous prescriptions are to be efficiently dispensed by pharmacists. However, the usual presence of several hundreds of similarly looking, but completely different types of blister packages, in a crowded dispensing station makes it prone to human error, posing serious safety and health concerns for a patients life. In this work, we propose a highlighted deep learning (HDL) based approach for accurate identification of blister packages. HDL allows smart manipulation on raw data in order to better segment the identified targets and to expose inherent descriptive features, thus facilitating an accurate deep learning based classification process. Specifically, HDL uses automatic detection and then segments and processes the raw blister pack images irrespective of position, lighting variations, etc., making them suitable for CNN to classify the correct blister pack types. A ResNet CNN classifier has been trained using the processed images, and the resultant CNN model is finally deployed for classification. We have conducted an extensive experiment at the adult lozenge dispensing station of MacKay Memorial Hospital. The database that will be released along with this paper, consists of 272 types of blister packages, with 65 images belonging to each type for training and additional 7 images for validation. On real testing scenario, the proposed approach yielded almost 100% accuracy, consistently over the entire testing set. The proposed solution can also be extended to any dispensing station for automatic detection and classification of blister packages, thereby offering an highly effective and efficient delivery system.