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In order to solve the problem of autonomous recognition of hexapod robot and realize the intelligent and humanized development of robot, OpenMV is taken as the main platform, hexapod robot is taken as the main machine carrier, Python is taken as the main development language, C language is taken as the auxiliary development language, and the reasonable application of image processing technology is added. A simple visual recognition system based on OpenMV is designed to realize the application of visual recognition.

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.

In the increasingly developing logistics industry, the application of intelligent logistics cars is becoming more and more common. This paper mainly introduces a design scheme of intelligent logistics cars based on the STM32F103ZET6 single-chip microcomputer and OpenMV programmable camera. Its main process is as follows. First, through OpenMV, the car can scan a QR code to read relevant information of the task. Then, OpenMV recognizes the color of the object, and the single-chip microcomputer controls the manipulator arm to grab the object of the corresponding color. Next, the car will carry the object to the designated position along the designated route. Finally, the process is repeated several times until the delivery task is completed. The study of intelligent cars has very important practical value and theoretical significance.

Due to the high cost of human labor for maintenance, garbages in rivers often do not receive immediate cleaning, resulting in severe water pollution. Those floating garbages simultaneously strip the aesthetic of rivers and threaten the local marine ecosystem. Thence, we developed an unmanned vessel based on Arduino’s platform, capable of auto-collecting garbage. The vessel includes two modes: the human operational mode allows individuals to manipulate the vessel and to collect garbages through Bluetooth connection; the auto-collecting mode is based on OpenMV’s video processing function, enabling vessels to detect garbages nearby and transport the garbages into the vessel’s interior through a rotating caterpillar track. At the end of this project, the vessel remains large rooms for improvements, such as increasing the efficiency and effectiveness of garbage collection and garbage detection. The vessel is suitable in small size rivers or ponds were artificial cleaning faces obstacles.

Star trackers are navigation sensors that are used for attitude determination of a satellite relative to certain stars. A star tracker is required to be accurate and also consume as little power as possible in order to be used in small satellites. While traditional approaches use lookup tables for identifying stars, the latest advances in star tracking use neural networks for automatic star identification. This manuscript evaluates two low-cost processors capable of running a star identification neural network, the Intel Movidius Myriad 2 Vision Processing Unit (VPU) and the STM32 Microcontroller. The intention of this manuscript is to compare the accuracy and power usage to evaluate the suitability of each device for use in a star tracker. The Myriad 2 VPU and the STM32 Microcontroller have been specifically chosen because of their performance on computer vision algorithms alongside being cost-effective and low power consuming devices. The experimental results showed that the Myriad 2 proved to be efficient and consumed around 1 Watt of power while maintaining 99.08% accuracy with an input including false stars. Comparatively the STM32 was able to deliver comparable accuracy (99.07%) and power measurement results. The proposed experimental setup is beneficial for small spacecraft missions that require low-cost and low power consuming star trackers.

With the widespread use of electric bicycles and batteries, fire accidents caused by batteries have become increasingly serious, especially in closed indoor environments. Traditional fire prevention methods often rely on static monitoring and simple sensors, which can suffer from delayed responses or fail to accurately identify fire hazards. To address this issue, this paper proposes an innovative monitoring system for preventing fire hazards caused by batteries or electric bicycles in indoor environments, based on an STMicroelectronics 32-bit Microcontroller (STM32) and Open-source Machine Vision module (OpenMV). The system uses deep learning to train a neural network to recognize the image information of batteries or electric bicycles. The key innovation of this system lies in several aspects: firstly, it utilizes the OpenMV module for real-time image processing, enabling efficient and accurate recognition of batteries and electric bicycles; secondly, the integration with the STM32 microcontroller enhances the system’s data processing capabilities and enables flexible communication and responses with external devices; finally, the system features high-efficiency serial communication, ensuring the real-time transmission and processing of monitoring data for swift responses to potential fire risks. Experimental results show that the system can accurately identify batteries or electric bicycles in indoor environments and respond in a timely manner, significantly reducing fire hazards. In addition, the system's design is not limited to preventing battery-related fire hazards in indoor environments. Compared to traditional methods, this study's innovation lies in combining deep learning and embedded control technology for fire prevention, providing a practical and scalable solution for battery-related fire risk prevention.

Image network design is a critical factor in image-based 3D shape reconstruction and data processing (especially in the application of combined SfM/MVS methods). This paper aims to present a new approach to designing and planning multi-view imaging networks for dynamic 3D scene reconstruction without preliminary information about object geometry or location. The only constraints are the size of defined measurement volume, the required resolution, and the accuracy of geometric reconstruction. The proposed automatic camera network design method is based on the Monte Carlo algorithm and a set of prediction functions (considering accuracy, density, and completeness of shape reconstruction). This is used to determine the camera positions and orientations and makes it possible to achieve the required completeness of shape, accuracy, and resolution of the final 3D reconstruction. To assess the accuracy and efficiency of the proposed method, tests were carried out on synthetic and real data. For a set of 20 virtual images of rendered spheres, completeness of shape reconstruction was up by 92.3% while maintaining accuracy and resolution at the user-specified level. In the case of the real data, the differences between predictions and evaluations for average density were in the range between 33.8% to 45.0%.

Aiming at the problem of light interference affecting image recognition, this paper designs a rolling ball control system based on image recognition. The system consists of the main controller Mega 2560, OpenMv image processing module, steering gear execution module, display module, and mechanical structure test bench. The system realizes that the small ball only relies on adjusting the tilt of the flat plate, completes the function of auto-interference, and quickly restores a stable static state. The system has good stability. The use of image recognition to identify and locate the ball has a good effect, and the control effect of the PID control algorithm on the ball has been debugged to achieve a good effect of stability, smoothness and high accuracy.

STM32F103 single-chip microcomputer is used as the control unit of the simulated electromagnetic squint. The single-chip microcomputer is used to control the function of launching shell and steering gear angle, and the steering gear is used to control the launching angle and direction of the electromagnetic squint. In the design of electromagnetic gun, coaxial coil gun is used as the main part, and two batteries are used as the energy supply device. Then the electromagnetic relay connected with the single-chip microcontroller controls the cycle charging. Firstly, 12V DC power is input into the circuit by the DC stabilized voltage power supply. After the current enters into the circuit, it is divided into two branches. One branch flows through the boosting circuit of electromagnetic relay to charge the thyristor capacitor. With the switch off, the instantaneous discharge of the capacitor generates an inverter magnetic field in the coil, which pushes the metal projectile out at a high speed. What the electromagnetic relay does is to boost the voltage. What the thyristor capacitor needs is high voltage current charging. Another branch supplies 5V DC power to the steering gear through the step-down electromagnetic relay to support the rotation of the steering gear. The system obtains the target position through openmv, and STM32 uses PWM wave to control the rotation angle and direction of the steering gear.