目的 解决定制化木门尺寸规格不统一、表面纹理多样而导致的堆垛分类困难、搬运效率低下等问题。方法 提出采用深度学习方法进行定制式木门工件检测，以YOLO V3网络为基本框架开展机器人工件识别方法研究。首先，通过图像数据增强和预处理，扩充定制式木门数据;然后，进行YOLO V3损失函数改进，并根据木门特征进行定制式木门数据集锚框尺度的重新聚类;最后，应用空间金字塔池化层进行YOLO V3中特征金字塔网络改进，并通过随机选取的测试集验证本文方法的有效性。结果 测试数据集的平均检测准确率均值达到98.05%，检测每张图片的时间为137 ms。结论 研究表明，本文方法能够满足木门生产线对准确率和实时性的要求，可大大提高定制化木门转线及堆垛效率。

The work aims to solve the problems such as the difficulty of stacking classification and the low handling efficiency caused by the non-uniform size and specification of customized wooden doors and the diversity of surface textures. A deep learning method was proposed to detect customized wooden door workpieces, and a robot workpiece recognition method was studied based on YOLO V3 network. First, through image data enhancement and preprocessing, the customized wooden door data were expanded. Then, the YOLO V3 loss function was improved, and the anchor frame scale of the customized wooden door data set was re-clustered according to the characteristics of the wooden doors. Finally, the spatial pyramid pooling layer was applied to improve the feature pyramid network in YOLO V3, and the effectiveness of this method was verified by a randomly selected test set. The average detection accuracy of the test data set reached 98.05%, and the detection time of each image was 137 ms. The research shows that this method can meet the requirements of the wooden door production line for accuracy and real-time nature, and can greatly improve the turning line and stacking efficiency of customized wooden doors.