基于改进的YOLOv8算法在洗碗机餐具识别上的研究

Dishwasher Tableware Recognition Based on Improved YOLOv8 Algorithm

本文探讨了基于改进的YOLOv8算法在识别不锈钢餐具方面的应用。YOLOv8算法是一种先进的深度学习模型,专用于实时物体识别。研究的主要创新点包括两个方面:首先,引入了Channel-Wise Mish模块,用以提升对图像中非线性特征的处理能力。这一改进有助于更准确地分类和识别不同的餐具。其次,优化的模型有效地缓解了深度学习中常见的梯度消失问题,从而提高了训练效率和检测准确率。本研究使用了消融实验方法,比较了改进前后的模型性能,验证了改进措施的有效性。此外,与其他常见模型如FPN、YOLOX和Dynamic R-CNN等相比,改进后的算法在准确性和泛化能力方面表现更佳。

This paper discusses the application of the improved YOLOv8 algorithm in the identification of stainless steel tableware.The YOLOv8 algorithm is an advanced deep learning model dedicated to real-time object recognition.The main innovations of this research focus on two aspects:First,Channel-Wise Mish module was introduced to improve the processing ability of nonlinear features in images.This improvement helps to classify and identify different tableware more accurately.Secondly,the optimized model effectively alleviates the common gradient disappearance problem in deep learning,thereby improving the training efficiency and detection accuracy.In this study,the ablation experiment was used to compare the performance of the model before and after the improvement,and to verify the effectiveness of the improved measures.In addition,compared with other common models such as FPN,YOLOX and Dynamic R-CNN,the improved algorithm performs better in terms of accuracy and generalization ability.