This article proposes a through-wall, multi-person action recognition framework based on the improved YOLOv5 network. First, we collected the through-wall multi-person motion echoes using the IR ...

This approach significantly enhances the diversity of object sizes, backgrounds, and perspectives within the dataset. By leveraging Mosaic Data Augmentation, YOLOv5 ... missing detection of small ...

RetinaNet is a one-stage object detection model that addresses the class imbalance problem using a focal loss function, improving detection accuracy for hard-to-detect objects. Faster R-CNN is a ...

YOLOv5 addresses these challenges with its highly optimized architecture, offering millisecond-level inference times while maintaining robust object ... workflow step, by invoking the packaged stack ...

OncoSeek is a multi-cancer early detection (MCED) test using seven protein ... cell-free DNA by shallow whole-genome sequencing. In a two-step approach, the initial screening is conducted using ...

Addressing the issues of low accuracy and slow detection speed in recognizing pointer meter readings ... Experiments were conducted using a self-made dataset of pointer-style instruments from nuclear ...

Our approach integrates lane and object detection algorithms to automate the aircraft taxiing process, ensuring collision avoidance and precise stopping. The navigation algorithm controls steering and ...

A complete and local NVR designed for Home Assistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Use of a Google Coral Accelerator ...

His object recognition software runs on a Raspberry Pi equipped with a webcam, and also makes use of Open CV. [Evan] notes that this opens up a lot of creative low-cost detection applications for ...