Pan Yucheng, Chen Jiasi, Wu Peiwen, Zhong Hongsheng, Deng Zihao, Sun Daozong
College of Electronic Engineering & College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China.
Sensors (Basel). 2025 Jun 4;25(11):3546. doi: 10.3390/s25113546.
Rail surface defects pose significant risks to the operational efficiency and safety of industrial equipment. Traditional visual defect detection methods typically rely on high-quality RGB images; however, they struggle in low-light conditions due to small, low-contrast defects that blend into complex backgrounds. Therefore, this paper proposes a novel defect segmentation method leveraging a dual-stream feature fusion network that combines polarization images with DeepLabV3+. The approach utilizes the pruned MobileNetV3 as the backbone network, incorporating a coordinate attention mechanism for feature extraction. This reduces the number of model parameters and enhances computational efficiency. The dual-stream module implements cascade and addition strategies to effectively merge shallow and deep features from both the original and polarization images. This enhances the detection of low-contrast defects in complex backgrounds. Furthermore, the CBAM is integrated into the decoding area to refine feature fusion and mitigate the issue of missing small-target defects. Experimental results demonstrate that the enhanced DeepLabV3+ model outperforms existing models such as U-Net, PSPNet, and the original DeepLabV3+ in terms of MIoU and MPA metrics, achieving 73.00% and 80.59%, respectively. The comprehensive detection accuracy reaches 97.82%, meeting the demanding requirements for effective rail surface defect detection.
钢轨表面缺陷对工业设备的运行效率和安全构成重大风险。传统的视觉缺陷检测方法通常依赖高质量的RGB图像；然而，由于小尺寸、低对比度的缺陷融入复杂背景中，它们在低光照条件下表现不佳。因此，本文提出了一种新颖的缺陷分割方法，该方法利用双流特征融合网络将偏振图像与DeepLabV3+相结合。该方法使用剪枝后的MobileNetV3作为骨干网络，并引入坐标注意力机制进行特征提取。这减少了模型参数数量，提高了计算效率。双流模块采用级联和加法策略，有效地融合原始图像和偏振图像的浅层和深层特征。这增强了对复杂背景中低对比度缺陷的检测。此外，将CBAM集成到解码区域，以优化特征融合并缓解小目标缺陷遗漏的问题。实验结果表明，改进后的DeepLabV3+模型在MIoU和MPA指标方面优于U-Net、PSPNet和原始DeepLabV3+等现有模型，分别达到了73.00%和80.59%。综合检测准确率达到97.82%，满足了钢轨表面缺陷有效检测的苛刻要求。
