Research on Object Recognition Approaches for Mobile Platforms with Limited Resources

This paper presents the results of a study of approaches to object detection for mobile platforms with limited computing resources. The research focuses on the development of software for real-time object identification using computer vision technologies optimized for embedded systems. The selected hardware platform is the ESP32-CAM, a low-power microcontroller with a built-in camera that allows for efficient video stream processing. The proposed approach involves the use of lightweight image processing methods and deep neural networks, in particular YOLO, adapted to work in resource-limited environments. Experiments confirm that the system can be implemented for real-world applications such as automated monitoring, security, and autonomous navigation.