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Abstract：
The identification of safflower filament targets and the precise localization of picking points are fundamental prerequisites for achieving automated filament retrieval. In light of challenges such as severe occlusion of targets, low recognition accuracy, and the considerable size of models in unstructured environments, this paper introduces a novel lightweight YOLO-SaFi model. The architectural design of this model features a Backbone layer incorporating the StarNet network; a Neck layer introducing a novel ELC convolution module to refine the C2f module; and a Head layer implementing a new lightweight shared convolution detection head, Detect_EL. Furthermore, the loss function is enhanced by upgrading CIoU to PIoUv2. These enhancements significantly augment the model\'s capability to perceive spatial information and facilitate multi-feature fusion, consequently enhancing detection performance and rendering the model more lightweight. Performance evaluations conducted via comparative experiments with the baseline model reveal that YOLO-SaFi achieved a reduction of parameters, computational load, and weight files by 50.0%, 40.7%, and 48.2%, respectively, compared to the YOLOv8 baseline model. Moreover, YOLO-SaFi demonstrated improvements in recall, mean average precision, and detection speed by 1.9%, 0.3%, and 88.4 frames per second, respectively. Finally, the deployment of the YOLO-SaFi model on the Jetson Orin Nano device corroborates the superior performance of the enhanced model, thereby establishing a robust visual detection framework for the advancement of intelligent safflower filament retrieval robots in unstructured environments.
摘要：
红花长丝目标的识别和采摘点的精确定位是实现自动提取长丝的基本前提。鉴于目标严重遮挡等挑战，低识别精度，以及非结构化环境中相当大的模型，本文介绍了一种新颖的轻量级YOLO-SaFi模型。该模型的架构设计具有合并了StarNet网络的Backbone层；颈部层引入了新颖的ELC卷积模块以完善C2f模块；头部层实现了新的轻量级共享卷积检测头，检测_EL。此外，通过将CIoU升级到PIoUv2来增强损失函数。这些增强显著增强了模型感知空间信息和促进多特征融合的能力，从而增强检测性能并使模型更加轻巧。通过与基线模型的比较实验进行的性能评估表明，YOLO-SaFi实现了参数的减少，计算负荷，重量文件减少了50.0%，40.7%,和48.2%，分别,与YOLOv8基线模型相比。此外，YOLO-SaFi展示了召回方面的改进，平均精度，检测速度提高1.9%，0.3%,每秒88.4帧，分别。最后,YOLO-SaFi模型在JetsonOrinNano设备上的部署证实了增强模型的卓越性能，从而为非结构化环境下智能红花丝取出机器人的进步建立了一个鲁棒的视觉检测框架。
