基于改进YOLOv7的密集褐菇检测算法研究

Research on Dense Detection Algorithm for Brown Mushroom based on Improved YOLOv7

摘要:

在褐菇种植工业化的复杂环境下,针对采摘机器人在密集生长的褐菇群的实时检测精度与速度低、误检率高等问题,提出一种基于改进YOLOv7的密集褐菇检测算法；为了防止网络退化,提高网络的检测精度与速率,降低网络的计算成本,引入ELAN_PS模块替换原ELAN模块；使用AFPN网络代替原网络的Neck 部分进行多尺度融合,为特征图分配不同的空间权重,提高模型对密集目标的划分能力；引入MDIoU损失函数作为算法的边界框损失函数,优化网络训练的收敛速度,提高了网络对密集遮挡的褐菇个体检测精度；将改进后的算法在自建的工业化种植褐菇数据集上进行训练与测试,与原YOLOv7相比,模型检测速度提高了2.1%,检测精确度提高了4.9%,平均精度mAP@0.5提高了9.1%。

Abstract:

In the complex environment of industrialized brown mushroom cultivation, a dense brown mushroom detection algorithm based on improved YOLOv7 is proposed to address the issues of low real-time detection accuracy and speed, and high false detection rate of picking robots in densely grown brown mushroom clusters. To prevent network degradation, improve the detection accuracy and speed of the network, and reduce the network"s computational cost, the ELAN_PS module is introduced to replace the original ELAN module. The AFPN network is used to replace the original network"s Neck part for multi-scale fusion, allocating different spatial weights to feature maps to enhance the model"s ability to separate dense targets. The MDIoU loss function is introduced as the algorithm"s bounding box loss function to optimize the convergence speed of network training and improve the detection accuracy of dense occluded brown mushroom individuals. The improved algorithm is trained and tested on a self-built industrialized brown mushroom dataset. Compared to the original YOLOv7, the model"s detection speed has increased by 2.1%, detection accuracy has increased by 4.9%, and average precision mAP@0.5 has increased by 9.1%.