#real-time processing

TengineKit, developed by OPEN AI LAB, is a versatile AI algorithm SDK for efficient integration and low-latency operations across mobile devices. Key features include face detection, 2D and 3D landmarks, and real-time hand and body detection. Its user-friendly API and compact design make it suitable for mobile applications. Frequent updates enhance capabilities, including recent improvements for Linux and Android performance. TengineKit is an effective tool for developers needing real-time AI functionalities on mobile platforms.

The Persian License Plate Recognition System (PLPR) effectively detects and recognizes Persian license plates, leveraging YOLOv5 models and a straightforward interface. It supports real-time processing for traffic surveillance and vehicle identification. The deep learning component enhances Persian character recognition accuracy, ensuring dependable performance across various conditions. Key features include adjustable video source settings, an easy-to-use GUI, and extensive management of residents and entrances, providing a comprehensive solution for vehicle access control and monitoring.

Segment Anything Model 2 (SAM 2) is designed for efficient image and video segmentation using a transformer architecture. By processing images as single-frame videos, SAM 2 offers extensive segmentation capabilities supported by the largest video dataset. The recent SAM 2.1 update includes new checkpoints and training tools, enhancing performance and usability.

The project presents a streamlined model for integrating three tasks into a single framework, emphasizing efficient real-time multi-task learning. It features an Adaptive Concatenate Module designed for segmentation and a universal segmentation head, providing notable performance in practical scenarios. Through extensive testing, the model demonstrates significant improvements over existing methods in terms of inference speed and visualization, using publicly available autonomous driving datasets and actual road data. The solution is implemented with Python and PyTorch, providing clear guidance for training, evaluation, and prediction, making it a practical choice for enhancing complex autonomous driving functions.