A Computer Vision System for Monitoring Disconnect Switches in Distribution Substations

Nassu, Bogdan Tomoyuki; Marchesi, Bruno; Wagner, Rafael Ramos; Gomes, Victor B.; Zarnicinski, Vanderlei; Lippmann, Lourival

doi:10.1109/tpwrd.2021.3071971

Cited by 16 publications

(10 citation statements)

References 18 publications

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“…Computer vision online workpiece inspection system belongs to the application of computer vision system in detecting parts, and the system is required to be able to accurately observe the target and make effective decisions on which parts can pass the inspection and which need to be discarded [19][20]. The computer vision online workpiece inspection system is shown in Fig.…”

Section: Composition Of Computer Vision Online Workpiece Inspection S...mentioning

confidence: 99%

Strategies for improving the detection accuracy of computerized machine vision considering spatial applications

Piao,

Song

2024

3C TIC

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In this paper, strategies in image preprocessing, hardware composition and detection methods are considered to improve computerized machine vision detection accuracy. First, image preprocessing and image enhancement are performed to improve the quality of the input image. Second, the hardware composition of the computer vision online inspection system is optimized by focusing on the light source selection and the performance of the image acquisition card in spatial applications. Combined with spatial application calculations, methods such as frequency domain method and Canny operator are used in order to improve the accuracy of machine vision detection. Finally, in the same test environment, the machine vision detection requires only 400MB and the detection accuracy ranges from 85.13% to 99.42%. With these comprehensive strategies, this paper provides a comprehensive and effective approach for computerized machine vision detection in spatial applications to improve detection accuracy and meet demanding application scenarios.

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Section: Composition Of Computer Vision Online Workpiece Inspection S...mentioning

confidence: 99%

Strategies for improving the detection accuracy of computerized machine vision considering spatial applications

Piao,

Song

2024

3C TIC

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“…In today’s digital era, computer vision, as a key branch of AI, is developing rapidly and playing an increasingly important role in many application fields. Especially in the task of image classification, high-performance vision processing systems are crucial to many industries, such as autonomous driving, medical diagnosis, and power inspection [ 1 , 2 , 3 ]. Recently, researchers have generally adopted complex and large-scale deep learning models in order to improve the classification performance of these systems [ 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Few-Shot Learning in Lightweight Models via Dual-Faceted Knowledge Distillation

Zhou,

Cheng,

Zhao

et al. 2024

Sensors

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In recent computer vision research, the pursuit of improved classification performance often leads to the adoption of complex, large-scale models. However, the actual deployment of such extensive models poses significant challenges in environments constrained by limited computing power and storage capacity. Consequently, this study is dedicated to addressing these challenges by focusing on innovative methods that enhance the classification performance of lightweight models. We propose a novel method to compress the knowledge learned by a large model into a lightweight one so that the latter can also achieve good performance in few-shot classification tasks. Specifically, we propose a dual-faceted knowledge distillation strategy that combines output-based and intermediate feature-based methods. The output-based method concentrates on distilling knowledge related to base class labels, while the intermediate feature-based approach, augmented by feature error distribution calibration, tackles the potential non-Gaussian nature of feature deviations, thereby boosting the effectiveness of knowledge transfer. Experiments conducted on MiniImageNet, CIFAR-FS, and CUB datasets demonstrate the superior performance of our method over state-of-the-art lightweight models, particularly in five-way one-shot and five-way five-shot tasks.

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“…Compared with the two-stage target detection framework, the single-stage detection framework has the advantages of simple structure and fast speed. Typical representatives are SSD [4] and Yolo [5] [6], they have achieved a balance between speed and accuracy. The latest single-stage detection framework Yolov5 achieves an accuracy comparable to the two-stage target detection framework.…”

Section: Introductionmentioning

confidence: 99%

A CNN-Based Single-Stage Occlusion Real-Time Target Detection Method

Liu,

Yang,

Liu

et al. 2024

JILSA

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Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.

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A Computer Vision System for Monitoring Disconnect Switches in Distribution Substations

Cited by 16 publications

References 18 publications

Strategies for improving the detection accuracy of computerized machine vision considering spatial applications

Strategies for improving the detection accuracy of computerized machine vision considering spatial applications

Enhancing Few-Shot Learning in Lightweight Models via Dual-Faceted Knowledge Distillation

A CNN-Based Single-Stage Occlusion Real-Time Target Detection Method

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