Open Access

Yi QinORCiD, Changyan PeiORCiD, Daiping SongORCiD, Junyu QiORCiD

• Accurate and real-time detection of gear pitting is crucial for industrial equipment health monitoring. However, existing segmentation methods encounter a fundamental trade-off between computational efficiency and segmentation precision. Heavyweight models achieve high accuracy but are impractical for real-time use, whereas lightweight architectures often fail to capture fine defect details. To address these challenges, we propose a vision-based measurement framework, termed detail feature calibration (DFC) bilateral segmentation network (DFCBNet), a lightweight segmentation network optimized for real-time gear inspection. The architecture incorporates three key components: (1) a DFC module, which enhances micro-defect sensitivity by calibrating multiscale semantic and spatial features; (2) a multiscale spatial detail attention module, which improves feature extraction for tiny pitting regions using parallel atrous convolutions and attention mechanisms; and (3) an adaptive feature fusion module, which adaptively compensates for detail loss during the decoding phase. Experimental results demonstrate that DFCBNet achieves 78.7% mean intersection over union (mIoU) on Cityscapes and 76.1% mIoU on Cambridge-driving Labeled Video Database, with real-time performance of 118 frames per second and only 2.38 M parameters. Furthermore, the proposed method demonstrates strong performance in practical gear-pitting measurement tasks, highlighting its potential for deployment in industrial online monitoring applications.