Aiming at the real-time object detection requirements of the intelligent control system for laboratory item transportation in mobile embedded unmanned vehicles, this paper proposes a lightweight hybrid detection system based on the OpenMV vision module. The system adopts a two-stage detection mechanism: in long-distance scenarios (>32 cm), fast target positioning is achieved through red threshold segmentation based on the HSV(Hue, Saturation, Value) color space; when in close range (≤32 cm), it switches to a lightweight deep learning model for fine-grained recognition to reduce invalid computations. By integrating the MobileNetV2 backbone network with the FOMO (Fast Object Matching and Occlusion) object detection algorithm, the FOMO MobileNetV2 model is constructed, achieving an average classification accuracy of 94.1% on a self-built multi-dimensional dataset (including two variables of light intensity and object distance, with 820 samples), which is a 26.5% improvement over the baseline MobileNetV2. In terms of hardware, multiple functional components are integrated: OLED display, Bluetooth communication unit, ultrasonic sensor, OpenMV H7 Plus camera, and servo pan-tilt. Target tracking is realized through the PID control algorithm, and finally, the embedded terminal achieves a real-time processing performance of 55 fps. Experimental results show that the system can effectively and in real-time identify and track the detection targets set in the laboratory. The designed unmanned vehicle system provides a practical solution for the automated and low-power transportation of small items in the laboratory environment.

With the rapid advancement of computer vision and deep learning technologies, the accuracy and efficiency of real-time gesture recognition have significantly improved. This paper introduces a gesture-controlled robot system based on thermal imaging sensors. By replacing traditional physical button controls, this design significantly enhances the interactivity and operational convenience of human–machine interaction. First, a thermal imaging gesture dataset is collected using Python3.9. Compared to traditional RGB images, thermal imaging can better capture gesture details, especially in low-light conditions, thereby improving the robustness of gesture recognition. Subsequently, a neural network model is constructed and trained using Keras, and the model is then deployed to a microcontroller. This lightweight model design enables the gesture recognition system to operate on resource-constrained embedded devices, achieving real-time performance and high efficiency. In addition, using a standalone thermal sensor for gesture recognition avoids the complexity of multi-sensor fusion schemes, simplifies the system structure, reduces costs, and ensures real-time performance and stability. The final results demonstrate that the proposed design achieves a model test accuracy of 99.05%. In summary, through its gesture recognition capabilities—featuring high accuracy, low latency, non-contact interaction, and low-light adaptability—this design precisely meets the core demands for “convenient, safe, and natural interaction” in rehabilitation, smart homes, and elderly assistive devices, showcasing clear potential for practical scenario implementation.