Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode

Ye, Tao; Wang, Baocheng; Ping, Song; Li, Juan

doi:10.3390/s18061916

Cited by 51 publications

(31 citation statements)

References 37 publications

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“…Both are computed as the sum of squared errors. In testing process, the non-maximum suppression (NMS) method [ 31 ] is applied to get rid of redundant bounding boxes.…”

Section: Methodsmentioning

confidence: 99%

Real-Time Detection of Railway Track Component via One-Stage Deep Learning Networks

Wang

Yang

Tsui

2020

Sensors

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Railway inspection has always been a critical task to guarantee the safety of the railway transportation. The development of deep learning technologies brings new breakthroughs in the accuracy and speed of image-based railway inspection application. In this work, a series of one-stage deep learning approaches, which are fast and accurate at the same time, are proposed to inspect the key components of railway track including rail, bolt, and clip. The inspection results show that the enhanced model, the second version of you only look once (YOLOv2), presents the best component detection performance with 93% mean average precision (mAP) at 35 image per second (IPS), whereas the feature pyramid network (FPN) based model provides a smaller mAP and much longer inference time. Besides, the detection performances of more deep learning approaches are evaluated under varying input sizes, where larger input size usually improves the detection accuracy but results in a longer inference time. Overall, the YOLO series models could achieve faster speed under the same detection accuracy.

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“…Both are computed as the sum of squared errors. In testing process, the non-maximum suppression (NMS) method [ 31 ] is applied to get rid of redundant bounding boxes.…”

Section: Methodsmentioning

confidence: 99%

Real-Time Detection of Railway Track Component via One-Stage Deep Learning Networks

Wang

Yang

Tsui

2020

Sensors

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“…CNNs have also been utilised to perform railcar safety inspection [154], determine the area of the rails ahead [155] detecting objects ahead [156], detect multiple catenary systems and support components [157]- [159], tracking joints [160] and detecting track defects [161], [162].…”

Section: Related Work In Railway Systemsmentioning

confidence: 99%

A Deep Learning Approach Towards Railway Safety Risk Assessment

Alawad

Kaewunruen

2020

IEEE Access

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Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks.

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“…Hu et al [35] combined traditional CNN with a spatial pyramid strategy to recognize the vehicle color under various conditions, but the accuracy was poor at night. Ye et al [36] proposed a CNN-based railway object detection network consisting of three connected modules, i.e., a depthwise-pointwise convolution module, coarse detection module and object detection module. Railway experiments showed that the network could distinguish straight, railway left, right, pedestrian, bullet train, and safety helmets.…”

Section: Object Detection With Cnnsmentioning

confidence: 99%

High-Speed Railway Clearance Intrusion Detection with Improved SSD Network

et al. 2019

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With the rapid development of high-speed railways, any objects intruding railway clearance will do great threat to railway operations. Accurate and effective intrusion detection is very important. An original Single Shot multibox Detector (SSD) can be used to detect intruding objects except small ones. In this paper, high-level features are deconvolved to low-level and fused with original low-level features to enhance their semantic information. By this way, the mean average precision (mAP) of the improved SSD algorithm is increased. In order to decrease the parameters of the improved SSD network, the L1 norm of convolution kernel is used to prune the network. Under this criterion, both the model size and calculation load are greatly reduced within the permitted precision loss. Experiments show that the mAP of our method on PASCAL VOC public dataset and our railway datasets have increased by 2.52% and 4.74% respectively, when compared to the original SSD. With our method, the elapsed time of each frame is only 31 ms on GeForce GTX1060.

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Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode

Cited by 51 publications

References 37 publications

Real-Time Detection of Railway Track Component via One-Stage Deep Learning Networks

Real-Time Detection of Railway Track Component via One-Stage Deep Learning Networks

A Deep Learning Approach Towards Railway Safety Risk Assessment

High-Speed Railway Clearance Intrusion Detection with Improved SSD Network

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