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Abstract：
In order to shorten detection times and improve average precision in embedded devices, a lightweight and high-accuracy model is proposed to detect passion fruit in complex environments (e.g., with backlighting, occlusion, overlap, sun, cloud, or rain). First, replacing the backbone network of YOLOv5 with a lightweight GhostNet model reduces the number of parameters and computational complexity while improving the detection speed. Second, a new feature branch is added to the backbone network and the feature fusion layer in the neck network is reconstructed to effectively combine the lower- and higher-level features, which improves the accuracy of the model while maintaining its lightweight nature. Finally, a knowledge distillation method is used to transfer knowledge from the more capable teacher model to the less capable student model, significantly improving the detection accuracy. The improved model is denoted as G-YOLO-NK. The average accuracy of the G-YOLO-NK network is 96.00%, which is 1.00% higher than that of the original YOLOv5s model. Furthermore, the model size is 7.14 MB, half that of the original model, and its real-time detection frame rate is 11.25 FPS when implemented on the Jetson Nano. The proposed model is found to outperform state-of-the-art models in terms of average precision and detection performance. The present work provides an effective model for real-time detection of passion fruit in complex orchard scenes, offering valuable technical support for the development of orchard picking robots and greatly improving the intelligence level of orchards.
摘要：
为了缩短检测时间,提高嵌入式设备的平均精度,提出了一种轻量级和高精度的模型来检测复杂环境中的百香果(例如，有背光，遮挡,重叠,sun,云，或下雨）。首先,用轻量级的GhostNet模型代替YOLOv5的骨干网络，减少了参数数量和计算复杂度，同时提高了检测速度。第二,在骨干网络中增加一个新的特征分支，并重建颈部网络中的特征融合层，以有效地结合低级和高级特征，这提高了模型的准确性，同时保持了其轻量级。最后,使用知识蒸馏方法将知识从能力较强的教师模型转移到能力较弱的学生模型，显著提高了检测精度。改进的模型表示为G-YOLO-NK。G-YOLO-NK网络的平均准确率为96.00%,比原来的YOLOv5s型号高出1.00%。此外，模型大小为7.14MB，原来型号的一半,在JetsonNano上实现时，其实时检测帧速率为11.25FPS。发现所提出的模型在平均精度和检测性能方面优于最先进的模型。本工作为复杂果园场景中百香果的实时检测提供了一种有效的模型，为果园采摘机器人的发展提供了宝贵的技术支撑,大大提高了果园的智能化水平。
