胡名鸿, 郭慧, 周邵萍, 刘亚菲. 轻量级垃圾回收机器人的视觉跟踪系统研究[J]. 微电子学与计算机, 2021, 38(11): 74-80. DOI: 10.19304/J.ISSN1000-7180.2021.0259
引用本文: 胡名鸿, 郭慧, 周邵萍, 刘亚菲. 轻量级垃圾回收机器人的视觉跟踪系统研究[J]. 微电子学与计算机, 2021, 38(11): 74-80. DOI: 10.19304/J.ISSN1000-7180.2021.0259
HU Minghong, GUO Hui, ZHOU Shaoping, LIU Yafei. Research on visual tracking system of lightweight garbage collection robot[J]. Microelectronics & Computer, 2021, 38(11): 74-80. DOI: 10.19304/J.ISSN1000-7180.2021.0259
Citation: HU Minghong, GUO Hui, ZHOU Shaoping, LIU Yafei. Research on visual tracking system of lightweight garbage collection robot[J]. Microelectronics & Computer, 2021, 38(11): 74-80. DOI: 10.19304/J.ISSN1000-7180.2021.0259

轻量级垃圾回收机器人的视觉跟踪系统研究

Research on visual tracking system of lightweight garbage collection robot

  • 摘要: 为增加垃圾拾取机器人的自主感知能力,提出了一种用于垃圾跟踪视觉系统的基于YOLOV4改进的轻量级目标检测算法YOLO-TrashNet。针对视觉跟踪系统速度与精度权衡问题,在YOLOV4的基础上将主干网络替换为MobileNetV3,分析了SE(Squeeze-and-Excitation)注意力机制、CBAM(Convolutional Block Attention Module)注意力机制以及CSP跨级局部网络结构对算法性能带来的影响。搭建了垃圾回收机器人视觉系统,使用了能提高目标定位能力Realsense深度相机,采集了公共场所最常见的15类垃圾,完成了室内垃圾跟踪实验。实验结果表明,提出的以CSPMobileNetV3-CBAM为主干网络的模型能大幅提升检测速度,与YOLO-V4相比计算量降低了93.3%,权重大小仅为19.5 MB,内存消耗低于YOLOV4-tiny;在Jetson Nano运行环境上相比YOLO-V4的垃圾检测牺牲了4%的精度,但是速度提升了6倍,mAP为86.3%。

     

    Abstract: To increase the autonomous perception ability of the garbage pickup robot, an improved lightweight target detection algorithm YOLO-TrashNet based on YOLOV4 for garbage tracking vision system is proposed. Aiming at the trade-off between speed and accuracy of the visual tracking system, the backbone network is replacedwith MobileNetV3 on the basis of YOLOV4, the effects of SE (Squeeze-and-Excitation) attention mechanism, CBAM (Convolutional Block Attention Module) attention mechanism and CSP cross-level local network structure on the performance of the algorithm are analyzed.Ithasbuilt a garbage collection robot vision system, used Realsense depth cameras that can improve target positioning, collected 15 most common types of garbage in public places, and completed indoor garbage tracking experiments.The experimental results shows that the CSPMobileNetV3-CBAM backbone network model proposed in this paper can greatly increase the detection speed, compared with YOLO-V4, the amount of calculation is reduced by 93.3%, the weight is only 19.5MB, and the memory consumption is lower than YOLOV4-tiny. Compared with YOLO-V4, the Garbage detection sacrifices 4% accuracy and its speed is increased by 6 times on Jetson Nano, its mAP is 86.3%. Provides a high-real-time and high-accuracy visual tracking system for garbage collection robots.

     

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