Novel Registration and Fusion Algorithm for Multimodal Railway Images with Different Field of Views

Guo, Baoqing; Zhou, Xingfang; Lin, Yingzi; Zhu, Liqiang; Yu, Zhiwu

doi:10.1155/2018/7836169

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…An image processing method including subtraction, binarization, morphological transformation and segmentation was used to track down moving obstacles at the level crossing [20]. A novel registration and fusion algorithm for multimodal railway images with different fields of views to detect objects was proposed by Guo and Zhou [21]. Tastimur et al [22] proposed an onboard objects detection method at intersection areas.…”

Section: Related Work Of Railway Clearance Intrusion Detectionmentioning

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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Section: Related Work Of Railway Clearance Intrusion Detectionmentioning

confidence: 99%

High-Speed Railway Clearance Intrusion Detection with Improved SSD Network

et al. 2019

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“…Compared with laser radar and millimeterwave radar, detection methods based on vision sensors have the advantages of low cost, convenient use and maintenance and good visualization effects, so they have been increasingly used in non-contact obstacle detection systems. Guo et al [6] proposed an automatic multi-modal registration and fusion algorithm for infrared and visible images with different fields of view to improve the detection accuracy of railway obstacles. Correct matching points are accumulated with the multiframe to overcome the insufficient matching points in a single image pair.…”

Section: Introductionmentioning

confidence: 99%

Detection method of obstacles in the dangerous area of electric locomotive driving based on MSE-YOLOv4-Tiny

Wang¹,

Wang²,

Guo³

et al. 2022

Meas. Sci. Technol.

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Aiming at the problems of error warning, low detection efficiency, and inability to meet the requirements of lightweight deployment in current track obstacle detection algorithms based on computer vision, the detection method of obstacles in the dangerous area of electric locomotive driving based on improved YOLOV4-Tiny (MSE-YOLOV4-Tiny) was proposed. The obstacle image dataset was constructed to provide a testing environment for various target detection algorithms. The method of perspective transformation, sliding window and least square cubic polynomial was used to fit the track line. By finding the area where the track was located and extending a certain distance to the outside of the track, the dangerous area of the electric locomotive running was obtained. A 3-scale detection structure was formed by increasing the shallow detection scale on the detection layer, so as to improve the detection accuracy of the network for smaller targets such as stones. The improved SKNet (ECA_SKNet) attention mechanism module was added to the output ends of the three scales of the backbone network, and the weight was re-assigned to realize feature reconstruction, thus further improving the detection accuracy of the target. By adding the Spatial Pyramid Pooling (SPP) module, the local and global features of the image were fused to improve the accurate localization ability and detection accuracy of the network. A comparative experiment was carried out on the dataset constructed in this paper. The experimental results show that the problem of false warnings caused by taking the target in the safe area as an obstacle can be effectively solved by dividing the danger area of electric locomotive driving. Compared with the original YOLOv4-Tiny algorithm, the MSE-YOLOv4-Tiny algorithm has a 3.97% increase in mAP while maintaining a higher detection speed and a smaller model memory. It has better detection performance and can be used for autonomous driving electric locomotive obstacle detection to provide technical support.

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