Research on the Lightweight Gear Surface Defect Detection Algorithm Based on BN-YOLOv5 - Zhao Xiaohui; Zhang Zhijie; Hu Sheng; Huan Kaixuan; Liu Lei; Pu Junping (Jixie chuandong)
A pretty crucial step in the manufacturing of gears is the defect detection on gear surfaces. An algorithmic detection model called BN-YOLOv5 which is based on an improved YOLOv5 is proposed in order to increase the accuracy of gear surface defect detection. Firstly, the technique strengthens the network's capacity to extract various features by embedding the weighted bidirectional feature pyramid network structure into the neck network structure. Secondly, a compact focus mechanism module, normalization-based attention module (NAM) is presented to its weighted bidirectional feature pyramid network structure which can more rapidly and efficiently fuse the feature information of higher and lower layers. Finally, the depth separable convolution module is used to replace every convolutional layer in the network structure, thereby lightening the network model. The experimental findings demonstrate that the enhanced algorithm model can achieve an average accuracy of 98.5%, a detection speed of 66 frames per second, and a modelling size of 9.69 MB, which effectively reduces the memory footprint of the model, and enables the task of real-time inspection of gear surface defects on small mobile devices.