Chongjie Leng

Semantic segmentation-based Complex Waterway Scene Understanding has shown great promise in the environmental perception of Unmanned Surface Vehicles. Existing methods struggle with estimating the edges of obstacles under conditions of blurred water surfaces. To address this, we propose the Lightweight Dual-branch Mamba Network (LDMNet), which includes a CNN-based Deep Dual-branch Network for extracting image features and a Mamba-based fusion module for aggregating and integrating global information. Specifically, we improve the Deep Dual-branch Network structure by incorporating multiple Atrous branches for local fusion; we design a Convolution-based Recombine Attention Module, which serves as the gate activation condition for Mamba-2 to enhance feature interaction and global information fusion from both spatial and channel dimensions. Moreover, to tackle the directional sensitivity of image serialization and the impact of the State Space Model’s forgetting strategy on non-causal data modeling, we introduce a Hilbert curve scanning mechanism to achieve multi-scale feature serialization. By stacking feature sequences, we alleviate the local bias of Mamba-2 towards image sequence data. LDMNet integrates the Deep Dual-branch Network, Recombine Attention, and Mamba-2 blocks, effectively capturing the long-range dependencies and multi-scale global context information of Complex Waterway Scene images. The experimental results on four benchmarks show that the proposed LDMNet significantly improves obstacle edge segmentation performance and outperforms existing methods across various performance metrics.

In order to meet the demands of autonomy and control optimization in solar greenhouse control systems, this paper developed an intelligent temperature and humidity control system for greenhouses based on the Single Neuron Proportional Integral Derivative (SNPID) algorithm. The system is centered around the Huada HC32F460 Micro-Controller Unit (MCU) and the RT-Thread operating system, integrated with the SNPID control algorithm. Through comprehensive simulation, model construction, and comparative experiments, this system was thoroughly evaluated in comparison with traditional PID control systems (cPID) that rely on overseas software and hardwsbuare. Simulation results show that our new system significantly outperforms traditional PID (Proportional Integral Derivative) systems in terms of temperature control stability and accuracy. Experimental data further confirm that, while ensuring cost-effectiveness, the new system achieves a remarkable 50.2% improvement in temperature and humidity control precision compared to traditional systems. The temperature Root Mean Square Error (RMSE) in the experimental greenhouse is 0.734 compared to 1.594 in the comparison greenhouse, indicating better stable temperature control capability. The vents in the experimental greenhouse have a maximum opening of 67 cm and a minimum of 5 cm, showing a quick response property to high temperatures. In contrast, the control greenhouse has a maximum vent opening of 55 cm, remaining unchanged during the test period, which reflects its slower response to temperature fluctuations. These results demonstrate the significant advantages of the designed solar greenhouse temperature and humidity control system in terms of autonomy and control optimization, providing an efficient and economical solution for solar greenhouse environmental management. This system shows significant practical application perspective in promoting intelligent agriculture and sustainable agricultural production, highlighting its broad impact and potential significance.

Measuring interpupilary distance and pupil height is a crucial step in the process of optometry. However, existing methods suffer from low accuracy, high cost, a lack of portability, and limited research on studying both parameters simultaneously. To overcome these challenges, we propose a method that combines ensemble regression trees (ERT) with the BlendMask algorithm to accurately measure both interpupillary distance and pupil height. First, we train an ERT-based face keypoint model to locate the pupils and calculate their center coordinates. Then, we develop an eyeglass dataset and train a BlendMask model to obtain the coordinates of the lowest point of the lenses. Finally, we calculate the numerical values of interpupillary distance and pupil height based on their respective definitions. The experimental results demonstrate that the proposed method can accurately measure interpupillary distance (IPD) and pupil height, and the calculated IPD and pupil height values are in good agreement with the measurements obtained by an auto-refractometer. By combining the advantages of the two models, our method overcomes the limitations of traditional methods with high measurement accuracy, low cost, and strong portability. Moreover, this method enables fast and automatic measurement, minimizing operation time, and reducing human errors. Therefore, it possesses broad prospects for application, particularly in the fields of eyeglass customization and vision inspection.

Wearing safety harness is essential for workers when carrying out work. When posture of the workers in the workshop is complex, using real-time detection program to detect workers wearing safety harness is challenging, with a high false alarm rate. In order to solve this problem, we use object detection network YOLOv5 and human body posture estimation network OpenPose for the detection of safety harnesses. We collected video streams of workers wearing safety harnesses to create a dataset, and trained the YOLOv5 model for safety harness detection. The OpenPose algorithm was used to estimate human body posture. Firstly, the images containing different postures of workers were processed to obtain 18 skeletal key points of the human torso. Then, we analyzed the key point information and designed the judgment criterion for different postures. Finally, the real-time detection program combined the results of object detection and human body posture estimation to judge the safety harness wearing situation within the current screen and output the final detection results. The experimental results prove that the accuracy rate of the YOLOv5 model in recognizing the safety harness reaches 89%, and the detection method of this study can ensure that the detection program accurately recognizes safety harnesses, and at the same time reduces the false alarm rate of the output results, which has high application value.