中文摘要：

针对工业分拣机器人识别复杂工件慢、精确度低以及定位不准等问题，提出一种基于深度学习的快速识别定位算法．通过工业高精度相机获取目标图像信息，经过图像灰度化、图像滤波、Otsu二值化处理，再经边界像素检测算法定位并分割目标图像．运用已训练的深度卷积神经网络（CNN）对目标进行识别，得到目标所在的位置坐标以及所属类别，实现工业机器人分拣．实验测试中以纹路复杂的象棋为例进行定位识别，结果表明定位算法误差小于0．8mm，最快识别速度达0．049秒／个，在实验环境中识别精度能保持在98％以上，表明算法具备良好的准确性和稳定性．

英文摘要：

To overcome the problems of slow recognition, low accuracy and inaccurate positioning for industrial sorting robots, a fast visual identification and location algorithm based on deep convolutional neural network （CNN） is proposed. Firstly, the target image information is obtained by an industrial precision camera, and the target image is located and seg- mented through graying, filtering, Otsu binarization and boundary detection of the images. Secondly, the target object is identified by using a trained CNN, and its position coordinate and class are obtained. Thus, target sorting by industrial robots is realized. Finally, the Chinese chess with complex lines are taken in test experiments to verify the identification and location algorithm. Experimental results show that the locating error is lower than 0.8 mm, the fastest recognition speed can reach 0.049 seconds per target, and the identification accuracy can be kept over 98% in the experimental environment. So, the proposed algorithm has good accuracy and stability.