Research on Wheel Hub Detection Algorithm Based on YOLOv8

鉴于传统轮毂分类检测流程中存在的劳动重复度高、人力成本高企及生产效率低下等挑战，文章提出了一种应用YOLOv8网络的汽车轮毂自动分类系统。该系统的工作流程包括：首先，收集丰富的汽车轮毂X光图像，构建一个囊括多种轮毂类型的综合性数据集；接着，利用YOLOv8算法对该数据集实施训练，以生成一个能够精确分辨轮毂种类的模型。在模型训练阶段，针对轮毂分类的具体特性，对YOLOv8算法进行了改进，引入了Focal Loss作为损失函数，从而有效缓解了正负样本不均衡的问题，进一步提升了轮毂分类的精确度。在实验验证环节，文章在不同噪声干扰和光照条件下对轮毂图像进行了测试。实验结果显示，该系统能迅速且准确地识别出各类轮毂，平均识别准确率高达98.43%，展现出了卓越的分类精度和强大的鲁棒性。In response to the challenges of high labor repetition, escalating labor costs, and low production efficiency in traditional hub classification and inspection processes, this paper proposes an automatic automobile hub classification system using the YOLOv8 network. The workflow of this system includes: first, collecting extensive X-ray images of automobile hubs to construct a comprehensive dataset encompassing various hub types; next, utilizing the YOLOv8 algorithm to train this dataset to generate a model capable of accurately distinguishing hub types. During the model training phase, improvements were made to the YOLOv8 algorithm based on the specific characteristics of hub classification, with Focal Loss introduced as the loss function, effectively mitigating the issue of imbalance between positive and negative samples and further enhancing the accuracy of hub classification. In the experimental validation stage, hub images were tested under different noise interference and lighting conditions. The experimental results demonstrate that the system can swiftly and accurately identify various types of hubs, with an average recognition accuracy of 98.43%, showcasing excellent classification accuracy and robustness.