Sugarcane stem node detection is one of the key functions of a small intelligent sugarcane harvesting robot, but the accuracy of sugarcane stem node detection is severely degraded in complex field environments when the sugarcane is in the shadow of confusing backgrounds and other objects.
To address the problem of low accuracy of sugarcane arise node detection in complex environments, this paper proposes an improved sugarcane stem node detection model based on YOLOv7. First, the SimAM (A Simple Parameter-Free Attention Module for Convolutional Neural Networks) attention mechanism is added to solve the problem of feature loss due to the loss of image global context information in the convolution process, which improves the detection accuracy of the model in the case of image blurring; Second, the Deformable convolution Network is used to replace some of the traditional convolution layers in the original YOLOv7. Finally, a new bounding box regression loss function WIoU Loss is introduced to solve the problem of unbalanced sample quality, improve the model robustness and generalization ability, and accelerate the convergence speed of the network.
The experimental results show that the mAP of the improved algorithm model is 94.53% and the F1 value is 92.41, which are 3.43% and 2.21 respectively compared with the YOLOv7 model, and compared with the mAP of the SOTA method which is 94.1%, an improvement of 0.43% is achieved, which effectively improves the detection performance of the target detection model.
This study provides a theoretical basis and technical support for the development of a small intelligent sugarcane harvesting robot, and may also provide a reference for the detection of other types of crops in similar environments.