Instance Segmentation Model for Substation Equipment Based on Mask R-CNN

Niu, Y.; Zhou, Taiji; Gu, Chunjie; Jiang, Anfeng; Lu, Wenlian

doi:10.1109/ceect50755.2020.9298600

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…Ma et al [1] designed a sample mask automatic labeling method based on thermal image guidance and a Progressive Optimization Model (POM), which achieved automatic labeling of instance masks and solved problems such as the difficulty of segmenting electrical equipment with complex structures. Yan et al [2] studied a substation equipment instance segmentation model based on Mask R-CNN, which has a better segmentation effect on the substation equipment image dataset. Li et al [17] proposed an IR insulator image segmentation algorithm based on dynamic mask and box annotation to achieve the overall segmentation of insulator strings by marking insulators with rectangular boxes in the infrared image.…”

Section: Literature Reviewmentioning

confidence: 99%

“…With the continuous development of modern industrial production, electrical equipment is the core component of industrial production, monitoring and maintaining its operating status to ensure production safety and efficiency is of great significance [1]. During the operation of electrical equipment, some faults that cannot be detected and processed promptly may lead to the shutdown of the electrical equipment, bringing huge economic losses to the enterprise, and accordingly the early-warning realization of online monitoring of electrical equipment and potential faults is important practically [2]. With the rapid development of computer vision and deep learning technology, instance segmentation technology has provided a more effective means for electrical equipment fault detection and diagnosis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Solov2 Based on Attention Mechanism and Weighted Loss Function for Electrical Equipment Instance Segmentation

Wu,

Liu,

Jiang

et al. 2024

CMC

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The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision. Because of the reliable, safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment, this paper uses the bottleneck attention module (BAM) attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation mode. Firstly, the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels, thereby improving the expression ability of the feature map; secondly, the weighted sum of CrossEntropy Loss and Dice loss is designed as the mask loss to improve the segmentation accuracy and robustness of the model; finally, the non-maximal suppression (NMS) algorithm to better handle the overlap problem in instance segmentation. Experimental results show that the proposed method achieves an average segmentation accuracy of mAP of 80.4% on three types of electrical equipment datasets, including transformers, insulators and voltage transformers, which improve the detection accuracy by more than 5.7% compared with the original Solov2 model. The segmentation model proposed can provide a focusing technical means for the intelligent management of power systems.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Solov2 Based on Attention Mechanism and Weighted Loss Function for Electrical Equipment Instance Segmentation

Wu,

Liu,

Jiang

et al. 2024

CMC

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show abstract

“…Zanthoxylum fruit target detection is similar to the majority of target detection programs in many aspects, such as UAVS automatic navigation, fire detection and face recognition. Therefore, traditional detection models, such as R-CNN [18][19][20], Faster R-CNN [21], YOLO [22][23][24][25] and SSD [26], have been applied to the detection of Zanthoxylum. Among these models, R-CNN, SSP-NET and Faster R-CNN have two detection stages, with high accuracy but much slower computing speed than YOLO and SSD models with primary structures.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Zanthoxylum Target Detection Method for an Intelligent Picking Robot under a Complex Background, Based on an Improved YOLOv5s Architecture

Huang

et al. 2022

Sensors

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The target recognition algorithm is one of the core technologies of Zanthoxylum pepper-picking robots. However, most existing detection algorithms cannot effectively detect Zanthoxylum fruit covered by branches, leaves and other fruits in natural scenes. To improve the work efficiency and adaptability of the Zanthoxylum-picking robot in natural environments, and to recognize and detect fruits in complex environments under different lighting conditions, this paper presents a Zanthoxylum-picking-robot target detection method based on improved YOLOv5s. Firstly, an improved CBF module based on the CBH module in the backbone is raised to improve the detection accuracy. Secondly, the Specter module based on CBF is presented to replace the bottleneck CSP module, which improves the speed of detection with a lightweight structure. Finally, the Zanthoxylum fruit algorithm is checked by the improved YOLOv5 framework, and the differences in detection between YOLOv3, YOLOv4 and YOLOv5 are analyzed and evaluated. Through these improvements, the recall rate, recognition accuracy and mAP of the YOLOv5s are 4.19%, 28.7% and 14.8% higher than those of the original YOLOv5s, YOLOv3 and YOLOv4 models, respectively. Furthermore, the model is transferred to the computing platform of the robot with the cutting-edge NVIDIA Jetson TX2 device. Several experiments are implemented on the TX2, yielding an average time of inference of 0.072, with an average GPU load in 30 s of 20.11%. This method can provide technical support for pepper-picking robots to detect multiple pepper fruits in real time.

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On automatic camera shooting systems via PTZ control and DNN-based visual sensing

Ren

Niu

et al. 2023

Intel Serv Robotics

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Instance Segmentation Model for Substation Equipment Based on Mask R-CNN

Cited by 7 publications

References 9 publications

An Improved Solov2 Based on Attention Mechanism and Weighted Loss Function for Electrical Equipment Instance Segmentation

An Improved Solov2 Based on Attention Mechanism and Weighted Loss Function for Electrical Equipment Instance Segmentation

A Real-Time Zanthoxylum Target Detection Method for an Intelligent Picking Robot under a Complex Background, Based on an Improved YOLOv5s Architecture

On automatic camera shooting systems via PTZ control and DNN-based visual sensing

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