摘要
提出一种基于改进YOLOv8算法的输送带损伤检测算法:用Focal Modulation模块替换YOLOv8原有的SPPF模块;针对损伤与背景相似度高的问题,引入DySample轻量动态上采样模块,使采样点集中在目标区域而忽略背景部分,实现损伤的有效识别;在颈部网络中加入高效多尺度注意力模块来获取更多细节信息,进一步提高损伤目标的关注度。引入PIoU v2损失函数,通过计算真实框与预测框之间的重叠面积精准定位损伤,同时考虑长宽比以更好地适应不同形状损伤。实验结果表明,改进后的模型对输送带损伤检测的精确度和平均精确度均值分别达到了90.3%和93.2%,相比于基线模型YOLOv8提高了2.3%和2.5%。改进YOLOv8的检测速度达83帧/s,可充分满足输送带损伤实时检测的需求。
A conveyor belt damage detection algorithm was proposed based on improved YOLOv8 algorithm.Focal Modulation module was used to replace the original spatial pyramid pooling-fast(SPPF)module.In view of the high similarity between the damage and the background,a DySample dynamic upsampling module was introduced to make the sampling points concentrate in the target areas and ignore the background parts,so as to achieve effective damage recognition.The efficient multi-scale attention(EMA)module was added to the neck network to obtain more detailed information and further improve the attention of injury targets.The PIoU v2 loss function was introduced to calculate the overlap areas between the real frame and the predicted frame,and the damage location was more accurate.Considering the aspect ratio might better adapt to the damage of different shapes.Experimental results show that the accuracy and mean average accuracy of the improved model for conveyor belt damage detection reach 90.3%and 93.2%respectively,which are 2.3%and 2.5%higher than the baseline model YOLOv8.The improved detection speed of YOLOv8 algorithm may reach 83 frames/s,which may fully meet the needs of real-time detection of conveyor belt damages.
作者
袁媛
白一超
周利东
孟文俊
王淼
曲文斌
YUAN Yuan;BAI Yichao;ZHOU Lidong;MENG Wenjun;WANG Miao;QU Wenbin(School of Vehicle and Traffic Engineering,Taiyuan University of Science and Technology,Taiyuan,030024)
出处
《中国机械工程》
北大核心
2025年第12期2829-2836,共8页
China Mechanical Engineering
基金
国家自然科学基金(52075356)
山西省科技成果转化引导专项(202204021301060)。
