The segmentation of abnormal regions is vital in smart manufacturing. The blurring sauce-packet leakage segmentation task (BSLST) is designed to distinguish the sauce packet and the leakage’s foreground and background at the pixel level. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby hindering the measurements of leakage area and impeding the automated sauce-packet production. To alleviate this issue, we propose the two-stage illumination-aware sauce-packet leakage segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose image enhancement to relieve the impact of uneven illumination, enhancing the texture details of the ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the multi-scale feature fusion module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by the MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by the adaptive weighting of spatial and channel dimensions. Furthermore, we generate a self-built dataset of sauce packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method.

ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method

Water leakage detection is of great significance to reduce economic losses and safety hazards in industrial sites. Aiming at the problem of water leakage detection caused by weak light condition and uneven light on turbine layer, this paper proposes a water leakage detection method based on weak light compensation for MaskRCNN turbine layer equipment. Firstly, due to the poor light of the turbine layer, the boundary of the seepage area photographed by the inspection robot is not clear, and the segmentation method is difficult to determine the target boundary. In this paper, low light enhancement algorithm is used to process the image of water seepage area taken by inspection robot to strengthen the boundary of water seepage image. Then, software Labelme was used to label the water seepage area of the enhanced inspection image and generate the configuration file corresponding to the inspection image. Thirdly, MaskRCNN instance segmentation network is introduced to train the image of the seepage area, and the network model of the detection task of the seepage area is generated. Finally, the test image is used to detect the water seepage area. After weak light enhancement, the trained MaskRCNN model is imported to obtain the detection result of the water seepage area. The experimental results show that the proposed method is effective and accurate, and it has a good application effect in detecting water dripping and leakage of turbine floor equipment by the wheel inspection machine in Qingyuan Energy Storage Power Generation Station.

Water leakage detection and its application of turbine floor equipment based on MaskRCNN

With the development and reform of electric power enterprises, the demand for electric power inspection personnel in power plant enterprises has increased. However, power generation companies have problems such as different training needs for maintenance personnel, training methods that do not conform to enterprise development, and mismatch between teaching materials and training work. At the same time, the differences in employees’ own professional skills and ability to receive knowledge have resulted in low training efficiency. In order to solve this situation, this paper analyzes the shortcomings of the existing training methods, systems and standards for power plant maintenance personnel. The mixed reality augmented technology was proposed to improve the training efficiency of power plant maintenance personnel.

Leveraging the strength of mixed reality for maintenance training -- A case study in large pumped storage power station

Multi-Gas Prediction Method for Transformer Oil Chromatography

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Water leakage detection and its application of turbine floor equipment based on MaskRCNN

Leveraging the strength of mixed reality for maintenance training -- A case study in large pumped storage power station

Multi-Gas Prediction Method for Transformer Oil Chromatography