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. 2024 Jun 1;24(11):3579.
doi: 10.3390/s24113579.

RSDNet: A New Multiscale Rail Surface Defect Detection Model

Jingyi Du  1 , Ruibo Zhang  1 , Rui Gao  1 , Lei Nan  1 , Yifan Bao  1
Affiliations

RSDNet: A New Multiscale Rail Surface Defect Detection Model

Jingyi Du et al. Sensors (Basel). .

Abstract

The rapid and accurate identification of rail surface defects is critical to the maintenance and operational safety of the rail. For the problems of large-scale differences in rail surface defects and many small-scale defects, this paper proposes a rail surface defect detection algorithm, RSDNet (Rail Surface Defect Detection Net), with YOLOv8n as the baseline model. Firstly, the CDConv (Cascade Dilated Convolution) module is designed to realize multi-scale convolution by cascading the cavity convolution with different cavity rates. The CDConv is embedded into the backbone network to gather earlier defect local characteristics and contextual data. Secondly, the feature fusion method of Head is optimized based on BiFPN (Bi-directional Feature Pyramids Network) to fuse more layers of feature information and improve the utilization of original information. Finally, the EMA (Efficient Multi-Scale Attention) attention module is introduced to enhance the network's attention to defect information. The experiments are conducted on the RSDDs dataset, and the experimental results show that the RSDNet algorithm achieves a mAP of 95.4% for rail surface defect detection, which is 4.6% higher than the original YOLOv8n. This study provides an effective technical means for rail surface defect detection that has certain engineering applications.

Keywords: BiFPN; CDConv; EMA; YOLOv8; rail surface defect detection.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The structure of YOLOv8.
Figure 2
Figure 2
The structure of the proposed method, RSDNet. (YOLOv8n-CDConv-BiFPN-EMA).
Figure 3
Figure 3
Comparison between Regular Convolution and Dilated Convolution. (a) is a regular convolution process (dilation rate = 1), and the receptive field is 3; (b) is the dilated convolution with dilation rate = 2, and the receptive field is 5; (c) is the dilated convolution with dilation rate = 3, and the receptive field is 7.
Figure 4
Figure 4
The structure of the Cascaded Dilated Convolution (CDConv).
Figure 5
Figure 5
Feature network design. (a) PANet; (b) BiFPN.
Figure 6
Figure 6
The structure of feature fusion.
Figure 7
Figure 7
EMA mechanism structure diagram.
Figure 8
Figure 8
Schematic of the location where the EMA module is added. (a) The structure of the Head; (b) The structure of added EMA Head.
Figure 9
Figure 9
Examples of RSDD datasets. (a) Rail surface images; (b) GroundTruth.
Figure 10
Figure 10
The mAP curves for the original YOLOv8 and the RSDNet.
Figure 11
Figure 11
Comparison of the Detection Effect of Each Algorithm.
See this image and copyright information in PMC

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