Research on gear flank surface defect detection of automotive transmissions based on improved YOLOv8

Abstract

Abstract In industrial production, the manufacturing processes may introduce defects on the gear flanks of transmission gears, potentially leading to premature failures and diminished performance. The early detection and precise assessment of surface defects on transmission gear flanks are critical for maintaining the safety, reliability, and cost-effectiveness of automobiles. At present, the principal approach for identifying defects on automotive transmission gear flanks predominantly involves manual visual inspections, supplemented by fluorescent magnetic particle testing. However, this approach suffers from low accuracy and efficiency. Consequently, this paper presents a defect detection algorithm that leverages an enhanced YOLOv8 model to facilitate the efficient detection of surface defects on automotive transmission gear flanks. Initially, the collected image data underwent data augmentation and exploratory analysis, which informed targeted enhancements. Subsequently, the YOLOv8 algorithm was thoroughly examined. The spatial pyramid pooling layer efficient architecture was incorporated into the backbone network, and the Deformable Convolutional Networks v4 module was integrated to boost the model’s capability in detecting irregular defects. In the neck network, the BiFormer attention mechanism was implemented to enhance detection performance for small-scale defects. Moreover, the newly developed modified adaptive structure feature fusion MASFF_Head structure was adopted as the detection head to augment detection efficacy for multi-scale defects. Additionally, the bounding box loss function was substituted with the Wise-Intersection over Union (WIoU) loss function to improve performance on low-quality samples. Experimental results demonstrated that the mean average precision (mAP@0.5) of the refined YOLOv8 network model reached 86.1%, marking a 2.8% increase over the original model and significantly boosting detection accuracy. When compared to other deep learning models, the enhanced YOLOv8 model exhibits considerable superiority in terms of detection precision and efficiency. The precision (P) value and recall (R) value achieved were 82.9% and 80.8%, respectively, with a detection time of 21.6 milliseconds. This underscores the method’s effectiveness and reliability in detecting automotive transmission gear defects, underscoring its pivotal role in facilitating automated detection processes on industrial production lines.