An Improved Faster R-CNN for High-Speed Railway Dropper Detection

Guo, Qifan; Liu, Lei; Xu, Wei; Gong, Yansheng; Zhang, Xuewu; Jing, Wenfeng

doi:10.1109/access.2020.3000506

Cited by 31 publications

(12 citation statements)

References 39 publications

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“…Differently, in reference [127], the authors mostly focused on droppers detection as they can be easily obscured by other catenary components. With the same aim, the authors of reference [129] implemented an approach for high-speed railways based on the Faster R-CNN architecture.…”

Section: Catenary Wiresmentioning

confidence: 99%

A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance

et al. 2022

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Within Artificial Intelligence, Deep Learning (DL) represents a paradigm that has been showing unprecedented performance in image and audio processing by supporting or even replacing humans in defect and anomaly detection. The Railway sector is expected to benefit from DL applications, especially in predictive maintenance applications, where smart audio and video sensors can be leveraged yet kept distinct from safety-critical functions. Such separation is crucial, as it allows for improving system dependability with no impact on its safety certification. This is further supported by the development of DL in other transportation domains, such as automotive and avionics, opening for knowledge transfer opportunities and highlighting the potential of such a paradigm in railways. In order to summarize the recent state-of-the-art while inquiring about future opportunities, this paper reviews DL approaches for the analysis of data generated by acoustic and visual sensors in railway maintenance applications that have been published until August 31st, 2021. In this paper, the current state of the research is investigated and evaluated using a structured and systematic method, in order to highlight promising approaches and successful applications, as well as to identify available datasets, current limitations, open issues, challenges, and recommendations about future research directions.

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Section: Catenary Wiresmentioning

confidence: 99%

A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance

et al. 2022

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“…RefineDet [12] and Retina Net [19] are also widely used. Guo et al [22] used a center-point rectangle loss function(CR loss) in Faster R-CNN to detect the droppers in high-speed railway. It takes the center points of bounding box and ground truth box as the vertex of the rectangle.…”

Section: Related Work a Object Detectionmentioning

confidence: 99%

Scale-Sensitive IOU Loss: An Improved Regression Loss Function in Remote Sensing Object Detection

Zhang

2021

IEEE Access

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Regression loss function in object detection model plays a important factor during training procedure. The IoU based loss functions, such as CIOU loss, achieve remarkable performance, but still have some inherent shortages that may cause slow convergence speed. The paper proposes a Scale-Sensitive IOU(SIOU) loss for the object detection in multi-scale targets, especially the remote sensing images to solve the problem where the gradients of current loss functions tend to be smooth and cannot distinguish some special bounding boxes during training procedure in multi-scale object detection, which may cause unreasonable loss value calculation and impact the convergence speed. A new geometric factor affecting the loss value calculation, namely area difference, is introduced to extend the existing three factors in CIOU loss; By introducing an area regulatory factor γ to the loss function, it could adjust the loss values of the bounding boxes and distinguish different boxes quantitatively. Furthermore, we also apply our SIOU loss to the oriented bounding box detection and get better optimization. Through extensive experiments, the detection accuracies of YOLOv4, Faster R-CNN and SSD with SIOU loss improve much more than the previous loss functions on two horizontal bounding box datasets, i.e, NWPU VHR-10 and DIOR, and on the oriented bounding box dataset, DOTA, which are all remote sensing datasets. Therefore, the proposed loss function has the state-of-the-art performance on multi-scale object detection.

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“…He et al [25] combined SSD [26] and Faster-RCNN to detect foreign matter in a high-speed train base. We [27] have also proposed an improved Faster-RCNN, which can accurately locate and identify the dropper of the OCS and has achieved great performance.…”

Section: A the Ocs Analysis And Fault Detectionmentioning

confidence: 99%

A Nested Residual Encoder-Decoder Network for Overhead Contact System Fastener Anomaly Detection

et al. 2021

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An overhead contact system (OCS) is key to providing power to high-speed railways. OCS detection is an important measure to ensure the safe operation of a high-speed railway. At present, OCS anomaly detection mainly relies on the manual analysis of the images regularly collected by the 4C system, which is very inefficient and can easily miss anomalies. Although some classification and object detection methods based on deep learning can be used for OCS anomaly detection, the effective training of deep networks can be difficult to support due to the small number of anomaly OCS image samples. Considering that most OCS faults are abnormal fasteners, we propose an abnormal detection method based on normal images, called the nested residual encoder-decoder network (NRE-Net). This network consists of two nested encoder-decoder networks, where the encoder is the shared part, and a residual structure is added to the encoding and decoding branches to enhance the feature expression ability. The experimental results show that the method can greatly improve the accuracy of anomaly detection for the CIFAR-10 dataset and OCS fastener dataset.

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An Improved Faster R-CNN for High-Speed Railway Dropper Detection

Cited by 31 publications

References 39 publications

A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance

A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance

Scale-Sensitive IOU Loss: An Improved Regression Loss Function in Remote Sensing Object Detection

A Nested Residual Encoder-Decoder Network for Overhead Contact System Fastener Anomaly Detection

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