To enhance the autonomy and accuracy of robotic systems, the R&D unit at Hoopad Company places special emphasis on the development of artificial intelligence and machine vision systems. These systems enable real-time analysis of live images, pattern recognition, obstacle detection, moving target tracking, and real-time decision-making in complex environmental conditions.
The use of convolutional neural networks (CNN), deep learning, object detection algorithms such as YOLO and SSD, combined with sensor data integration, has enabled Hoopad’s robots to perform high-level missions effectively. These capabilities are especially applied in areas such as fire detection, border surveillance, infrastructure inspection robots, and precise mapping systems.