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

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 the 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 15.5 %, detection accuracy has increased by 6.4 %, and average precision mAP@0.5 has increased by 6.9 %.