Object recognition and grabbing detection based on deep learning
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摘要: 针对机械臂抓取在工业生产中的复杂作业环境、不同零件之间存在干扰的问题，文章提出了一种基于深度学习的目标识别及抓取方法，以此来减少抓取场景中物体位置的不确定性，提 高检测和抓取成功率。采用卷积注意力机制模块（convolutional block attention module，CBAM）对YOLO-V5进行改进，加强卷积网络对图像特征的关注和提取能力，提高检测精度。改进之后的网络平均识别率提高了5.26%，证明了改进是有效且成功的。通过AUBO-i5机械臂、电动夹爪、相机以及六轴力传感器等设备搭建了一套机械臂抓取系统，实验结果表明所提出的方法在实际抓取中可以适应不同的抓取场景，减少外界干扰，提高抓取成功率，具有良好的应用前景。Abstract: Aiming at the problem of complex operating environment and interference between different parts in the industrial production of robotic arm grasping, this paper proposes a target recognition and grasping method based on deep learning, so as to reduce the uncertainty of object position in the grasping scene and improve the success rate of detection and grasping. The Convolutional Block Attention Module (CBAM) is used to improve YOLO-V5 to enhance the attention and extraction ability of convolutional networks on image features and improve the detection accuracy. The average recognition rate of the improved network increased by 5.26%, proving that the improvement was effective and successful. In this paper, a set of robotic arm grasping system is built by AUBO-i5 manipulator, electric gripper, camera, and six-axis force sensor, and the experimental results show that the proposed method can adapt to different grasping scenarios in real grasping, reduce external interference, improve the success rate of grasping, and have good application prospects.
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Key words:
- YOLO-V5 /
- deep learning /
- robotic arm grabbing /
- object detection /
- CBAM
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表 1 算法对比表
算法 mAP/（%） 速度 Lenz I等 93.7 13.5 s YOLO-V3 92.33 / YOLO-V3改 92.65 / Morrison D等 88 20 ms YOLO-V5 88.62 32 ms 本文 93.88 35 ms 
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表 2 实验抓取结果
抓取场景 光照
环境YOLO-V5 添加CBAM模块的
YOLO-V5检测准确
率/（%）抓取成功
率/（%）检测准确
率/（%）抓取成功
率/（%）无杂物干扰下
目标物体的识
别和抓取环境一 97.1 95.6 100 97.1 环境二 94.3 95.4 100 95.7 环境三 91.4 93.8 95.7 94.1 有杂物干扰下
目标物体的识
别的抓取环境一 95.7 94.1 97.1 94.1 环境二 90.0 92.1 94.3 92.4 环境三 85.7 88.3 90.0 90.4
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