针对自然环境下果实重叠、相互遮挡和目标小的澳洲坚果果实检测准确率低的问题,提出一种改进YOLOv9 模型的识别方法(YOLO-L).首先,引入BiFormer注意力机制,该机制通过双层路由注意力机制实现了动态、查询感知的稀疏注意力分配,能够很好地捕获特征表征,增强网络对全局特征的关注度;其次,采用VoVGSCSP模块代替YOLOv9 中的CBFuse模块,提高了复杂场景下小目标的检测效果;最后,将YOLOv9 模型默认的损失函数替换成排斥损失函数,解决了果实排列密集和漏检的问题,进一步提升了澳洲坚果果实检测的平均精度.通过消融试验和对比试验来验证模型的有效性,发现YOLO-L模型的平均精度均值、精确率、召回率和 F1 值分别达到96.2%、92.3%、88.2%和 90.2%.与YOLOv9 模型相比,YOLO-L模型的平均精度均值提升了 4.9 个百分点.总体而言,YOLO-L模型能够在自然环境下准确识别被遮挡、重叠的澳洲坚果果实,且检测精度高.研究结果可为澳洲坚果产业的智能采摘提供有效的技术支持.
Macadamia(Macadamia integrifolia Maiden&Betche)detection and rec-ognition in natural environments based on YOLO-L
Aiming at the issue of low detection accuracy for macadamia nuts in natural environments due to overlapping,mutual occlusion,and small targets,an improved YOLOv9 model recognition method(YOLO-L)was proposed.Firstly,the Bi-Former attention mechanism was introduced,which achieved dynamic and query-aware sparse attention allocation through the Bi-level routing attention mechanism.This mechanism was capable of effectively capturing feature representations and enhanced the network's focus on global features.Secondly,the VoVGSCSP module was used to replace the CBFuse module in YOLOv9,which improved the detection performance for small targets in complex scenes.Lastly,the default loss function of the YOLOv9 model was replaced with an exclusion loss function,which solved the problems of dense fruit arrangement and missed detections,and further enhanced the average accuracy of macadamia nut detection.The effectiveness of the model was validated through ablation and comparative experiments.It was found that the mean average precision,precision,recall,and F1 score of YOLO-L model reached 96.2%,92.3%,88.2%,and 90.2%,respectively.Compared with the YOLOv9 model,the mean average precision of the YOLO-L model was im-proved by 4.9 percentage points.Overall,the YOLO-L model can accurately identify occluded and overlapped macadamia nuts in natural environments with high detection accuracy.The re-search results can provide effective technical support for the intelligent harvesting in the macadamia industry.
万方数据
维普
