SP-RTSD: A Lightweight Real-Time Strawberry Detection on Edge Devices for Onboard Robotic Harvesting

IF 5.2 2区 计算机科学 Q2 ROBOTICS
Yujie Chen, Aijing Shu, Zhanhao Liu, Yang Chen, Won Suk Lee, Yanchao Zhang
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Abstract

On-board strawberry-picking robots offer the potential to significantly reduce labor costs and enhance picking efficiency. How to achieve high precision and fast strawberry recognition on resource-constrained edge devices is the key to robotic strawberry harvesting. Before developing our model, two lightweighting methods that maintain model structure are explored to substantiate the thesis that only judicious compression strategies tailored to edge hardware specifications can transform heavyweight deep models into efficient and compact deployments with enhanced performance on embedded devices. Based on this, and in combination with RTSD, Superb Real-time Strawberry Detection (SP-RTSD), which is designed to achieve faster and more accurate strawberry recognition on edge devices. Firstly, the C2f-Faster module performs channel-wise feature screening to enhance feature extraction efficiency while reducing model parameters; secondly, a lightweight recognition head with a parameter sharing mechanism is proposed specially for the edge devices. The speed of SP-RTSD was significantly improved by 22% from 20.63 to 25.18 FPS, which is similar to the 25.2 FPS of RTSD. Without changing the model structure, the model size is reduced by 40.3% from 6.2 to 3.7 MB. In contrast to typical lightweight strategies, which often boost inference speed at the cost of accuracy, SP-RTSD achieves exceptional accuracy with a mean average precision (mAP) of 91.7%, slightly outperforming the original baseline model (90.7%). The improvements in accuracy, speed, and size demonstrate that SP-RTSD addresses the challenge of balancing accuracy with inference speed on edge devices. In comparison experiments with other advanced object detection and lightweight models, as well as tests on additional open-source strawberry data sets, SP-RTSD consistently delivered superior results, affirming its robustness. Furthermore, SP-RTSD demonstrated an impressive combined success rate of 92% in strawberry grasping simulation experiments with a robotic arm, thereby confirming its suitability for integration into practical picking machines.

Abstract Image

SP-RTSD:用于机载机器人收割的边缘设备上的轻量级实时草莓检测
机载草莓采摘机器人提供了显著降低劳动力成本和提高采摘效率的潜力。如何在资源受限的边缘设备上实现高精度、快速的草莓识别是机器人草莓采摘的关键。在开发我们的模型之前,我们探索了两种保持模型结构的轻量化方法,以证实只有针对边缘硬件规格量身定制的明智压缩策略才能将重量级深度模型转换为高效紧凑的部署,并在嵌入式设备上增强性能。在此基础上,结合RTSD技术,SP-RTSD (Superb Real-time Strawberry Detection)能够在边缘设备上实现更快、更准确的草莓识别。首先,C2f-Faster模块对通道进行特征筛选,在降低模型参数的同时提高特征提取效率;其次,针对边缘设备,提出了一种具有参数共享机制的轻量化识别头;SP-RTSD的速度从20.63 FPS提高到25.18 FPS,显著提高了22%,与RTSD的25.2 FPS相当。在不改变模型结构的情况下,模型大小从6.2 MB减少到3.7 MB,减少了40.3%。与通常以精度为代价提高推理速度的典型轻量级策略相比,SP-RTSD的平均精度(mAP)达到了91.7%,略优于原始基线模型(90.7%)。精度、速度和尺寸方面的改进表明,SP-RTSD解决了在边缘设备上平衡精度和推理速度的挑战。在与其他高级目标检测和轻量级模型的对比实验中,以及在其他开源草莓数据集上的测试中,SP-RTSD始终提供了优越的结果,证实了其鲁棒性。此外,SP-RTSD在机械臂抓取草莓模拟实验中显示出92%的令人印象印象的综合成功率,从而证实了其集成到实际采摘机中的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Field Robotics
Journal of Field Robotics 工程技术-机器人学
CiteScore
15.00
自引率
3.60%
发文量
80
审稿时长
6 months
期刊介绍: The Journal of Field Robotics seeks to promote scholarly publications dealing with the fundamentals of robotics in unstructured and dynamic environments. The Journal focuses on experimental robotics and encourages publication of work that has both theoretical and practical significance.
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