Embedded YOLO v8: Real-time detection of sugarcane nodes in complex natural environments by rapid structural pruning method

In the exploring of sugarcane intelligent harvesting technology, the target detection task of sugarcane node faces serious challenges on low detection accuracy affected by complex natural environment and the time-consuming task to lightweight algorithm structure. In addition, the classical target detection model, such as YOLO (You Only Look Once), with requirement of huge computationally ability puts huge computing pressure to the embedded device for sugarcane nodes detection. In order to solve these problems, this study proposed an improved YOLOv8n-ghost, which adopted Ghost module to build lightweight network and reduce model redundancy while ensuring performance. The dataset was extended and diversified by data classification and enhancement to increasing the robustness and generalization ability of the model. The structured pruning method based on DepGraph (Dependency Graph) was adopted to compress the optimized model, which greatly reduced the complexity of the model. To reduce experimentation time and computational cost, a rapid method for the optimal pruning rate was proposed by combination of big-small step size to quickly achieve the optimal pruning rate with shorter experiment time and satisfied accuracy. By comparison of different pruning methods and lightweight methods, the AP (Average Precision) of model Pruned 65 %-YOLOv8n-ghost was > 90 % with only 0.53 M parameters and 2.2G FLOPs. Finally, the model was accelerated with TensorRT and tested in the embedded device. The real-time speed of the improved model approached 30 frames per second, which was 200 % higher than before and met the requirements of real-time detection of sugarcane node.