2021
DOI: 10.1109/tie.2020.3021640
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Application of Lightweight Railway Transit Object Detector

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Cited by 22 publications
(11 citation statements)
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References 26 publications
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“…Dilated convolution is a type of convolutional operation that allows the network to have a larger receptive field without adding extra parameters. YOLOF [38], RFB-Net [51], LFD-Net [29] and SA-YOLOv3 [14] combined the dilated convolution module with deep layers of the network, improving detection accuracy while significantly increasing the model parameters. We believe that a model with high detection accuracy and low latency, while also having fewer parameters, is undoubtedly more promising for traffic object detection.…”
Section: Receptive Field Enlargement Methodsmentioning
confidence: 99%
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“…Dilated convolution is a type of convolutional operation that allows the network to have a larger receptive field without adding extra parameters. YOLOF [38], RFB-Net [51], LFD-Net [29] and SA-YOLOv3 [14] combined the dilated convolution module with deep layers of the network, improving detection accuracy while significantly increasing the model parameters. We believe that a model with high detection accuracy and low latency, while also having fewer parameters, is undoubtedly more promising for traffic object detection.…”
Section: Receptive Field Enlargement Methodsmentioning
confidence: 99%
“…A top-down pathway and lateral connections were introduced to aggregate multi-scale features for detecting different scale objects. Following the FPN structure, a large number of detection models have been proposed to achieve impressive detection performance [9,14,[26][27][28][29]. Based on the FPN structure, PANet [30] added a bottomup pathway to enhance the representation of lower feature levels.…”
Section: Multi-scale Detectionmentioning
confidence: 99%
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“…In the railway industry, the use of cameras to accurately and quickly detect targets is an important but challenging problem. Ye et al [36] proposed the LFD algorithm, which improves the real-time detection accuracy of targets of different scales (especially small targets) without additional storage space and processing time, and is used for collision warning in the train safety system. Wang et al [37] introduced Region-of-Interest into the YOLOv4 network to enhance train detection of pedestrians and signal lights in highly complex and harsh environments.…”
Section: B Application Of Object Detection In Various Industriesmentioning
confidence: 99%
“…He et al [26] adopted D-CSPDarkNet as the backbone network to improve the YOLOv4 model and enhanced network performance by transfer learning and phased training strategies. Ye et al [27] designed a stable sampling module, lightweight feature extraction module, and feature fusion module to optimize the detector performance, which enabled robust detection in various rail transit scenarios. Chandan et al [28] applied the lightweight MobileNet to improve the single shot multibox detector (SSD) and used image processing methods in OpenCV to complete the detection and tracking of the objects.…”
Section: Related Workmentioning
confidence: 99%