Real-Time Obstacle Detection Over Railway Track using Deep Neural Networks

Rahman, Fahim Ur; Ahmed, Md. Tanvir; Hasan, Md. Mehedi; Jahan, Nusrat

doi:10.1016/j.procs.2022.12.031

Cited by 6 publications

(1 citation statement)

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“…Based on improved YOLOv3, Ye et al [30] proposed a detection method that uses a stable lightweight feature extraction network and an adaptive feature fusion network to provide richer feature information, thus achieving good detection results for multiple types of obstacles. Rahman et al [31] applied a variant of MobileNetV2 to detect the clearance condition of the railway area, and designed a series of data augmentation operations to improve the training effect. Zhang et al [32] designed a train intelligent detection system that fuses camera and lidar data to achieve high-precision track area segmentation and obstacle detection for all weather conditions.…”

Section: Related Workmentioning

confidence: 99%

An improved single-stage convolutional neural network for rail transit obstacle detection

Qin,

He,

Sun

et al. 2023

Meas. Sci. Technol.

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Obstacles that intrude into the rail area can lead to serious rail accidents, so obstacle detection technology is an essential guarantee for the safe operation of fully automatic trains. To meet the high-performance requirements of onboard obstacle detection, an efficient feature-aware convolutional neural network (EFA-Net) is proposed in this paper. The MA-FPN is designed as feature fusion network to extract multi-scale context information. In the detection head, the dynamic awareness block is used to refine the features. A joint representation branch and the generalized focal loss function are introduced to optimize the training effect. The experiments are based on the dataset of real-world rail transit environment. The results show that EFA-Net can achieve a detection accuracy of 90.4% mAP at a detection speed of 20.4 FPS, and the lightweight design significantly reduces the computational complexity of the proposed model. Compared with other classical detectors, EFA-Net has the best comprehensive performance.

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Section: Related Workmentioning

confidence: 99%