基于轻量化YOLO v8s-GD的自然环境下百香果快速检测模型

Passion Fruit Rapid Detection Model Based on Lightweight YOLO v8s-GD

为了提高百香果检测精度,并将深度学习模型部署在移动平台上,实现快速实时推理,本文提出一种基于改进YOLO v8s的轻量化百香果检测模型(YOLO v8s-GD)。使用聚集和分发机制(GD)替换颈部特征融合网络,提高模型对百香果图像特征信息跨层融合能力和模型泛化能力;通过基于层自适应幅度的剪枝(LAMP)修剪模型,损失一定精度换取减小模型体积,减少模型参数量,以实现在嵌入式设备上快速检测;运用知识蒸馏学习策略弥补因剪枝而损失的检测精度,提高模型检测性能。实验结果表明,对于自然环境下采集的百香果数据集,改进后模型参数量和内存占用量相比原YOLO v8s基线模型分别降低63.88%和62.10%,精确率(Precision)和平均精度(AP)相较于原模型分别提高0.9、2.3个百分点,优于其他对比模型。在Jetson Nano和Jetson Tx2嵌入式设备上实时检测帧率(FPS)分别为5.78、19.38 f/s,为原模型的1.93、1.24倍。因此,本文提出的改进后模型能够有效检测复杂环境下百香果目标,为实际场景中百香果自动采摘等移动端检测设备部署和应用提供理论和技术支持。

In order to improve the accuracy of passion fruit detection and deploy the deep learning model on mobile platforms for rapid real-time inference,a lightweight passion fruit detection model was proposed based on an improved YOLO v8s.The model replaced the neck feature fusion network with a Gather-and-distribute mechanism(GD)to enhance cross-layer feature fusion and generalization capabilities for passion fruit images.Additionally,the model was pruned by using layer-adaptive sparsity for the magnitude-based pruning(LAMP),which traded off some accuracy to reduce model size and parameter count,facilitating rapid detection on embedded devices.Knowledge distillation was employed to compensate for the accuracy loss caused by pruning,further enhancing detection performance.Experimental results showed that for a passion fruit dataset collected in natural environments,the improved model reduced parameter count and memory usage by 63.88%and 62.10%,respectively,compared with the original YOLO v8s baseline model.The precision and average precision(AP)of the improved model were increased by 0.9 percentage points and 2.3 percentage points,respectively,outperforming other comparative models.Real-time detection frame rates(FPS)on Jetson Nano and Jetson Tx2 embedded devices were 5.78 f/s and 19.38 f/s,respectively,which were 1.93 times and 1.24 times higher than that of the original model.Therefore,the proposed improved model effectively detects passion fruit in complex environments,providing theoretical and technical support for the deployment and application of mobile detection devices in scenarios such as automatic passion fruit harvesting.