YDRSNet: an integrated Yolov5-Deeplabv3 + real-time segmentation network for gear pitting measurement

The traditional calculation methods of the pitting area ratio include artificial vision inspection and rubbing measurement based on cutting tooth. However, these methods have the disadvantages of low efficiency, high cost and static measurement. The non-contact computer vision measurement technology can achieve continuous monitoring without interfering with the machine operation, and have satisfactory detection accuracy. In this paper, we propose an integrated Yolov5-Deeplabv3 + real-time segmentation network (YDRSNet) for gear pitting measurement. The two-stage network is constructed by using Yolov5 and an improve Deeplabv3 + , which can be applied to process the video samples in real time and overcome the problem of sample imbalance. Considering that the second-stage network implements a binary classification task, the dice loss is applied to replace the Cross-entropy loss for reducing the amount of calculation and solving the problem of sample imbalance effectively. Moreover, a DC-Focus module is embedded into the second-stage network for reducing the information loss caused by down sampling. Compared with the existing typical segmentation algorithms, the proposed YDRSNet has stronger segmentation ability, and it can segment the effective tooth surface and different levels of pitting quickly and accurately. The proposed methodology provides a feasible way for online measuring the pitting area ratio and detecting the degree of gear failure.