针对蟹味菇生产过程中更好地预估产量,对生长状态做到实时检测的问题,提出了一种基于改进YOLOv8卷积神经网络的蟹味菇识别检测方法.该方法参照PASCAL VOC数据集格式,构建了蟹味菇目标检测数据集,采用添加CBAM注意力机制对原算法进行改进,并且与Faster R-CNN、SSD(single shot multibox detector)、原始YOLOv8等算法进行模型性能的试验对比.试验结果表明,改进的算法明显优于其他算法,其在测试集上的平均精度均值(mean average precision,mAP)和检测速度分别达到 95%和 91帧/s.此检测精度与检测时间满足蟹味菇的实时识别检测任务,为预估蟹味菇产量,提高生产管理水平提供了理论技术支持.
Crab Flavored Mushroom Detection Method Based on Improved YOLOv8 Convolutional Neural Network
A crab flavored mushroom recognition and detection method based on improved YOLOv8 convolutional neural network was proposed to address issue of better yield estimation and real-time detection of growth status in production process of crab flavored mush-rooms.This method refered to PASCAL VOC dataset format and constructed a crab flavored mushroom target detection dataset.Origin-al algorithm was improved by adding CBAM attention mechanism,and model performance was compared with Faster R-CNN,SSD(single shot multibox detector),original YOLOv8 and other algorithms for experimental testing.Experimental results showed that improved algorithm was significantly superior to other algorithms,with an mean average precision(mAP)and detection speed of 95%and 91 frames/s on the test set,respectively.This detection accuracy and detection time met real-time recognition and detection task of crab flavored mushrooms,providing theoretical and technical support for estimating yield of crab flavored mushrooms and im-proving production management level.
万方数据
维普
