In this contribution, we introduce a novel ensemble method for the re-identification of industrial entities, using images of chipwood pallets and galvanized metal plates as dataset examples. Our algorithms replace commonly used, complex siamese neural networks with an ensemble of simplified, rudimentary models, providing wider applicability, especially in hardware-restricted scenarios. Each ensemble sub-model uses different types of extracted features of the given data as its input, allowing for the creation of effective ensembles in a fraction of the training duration needed for more complex state-of-the-art models. We reach state-of-the-art performance at our task, with a Rank-1 accuracy of over 77% and a Rank-10 accuracy of over 99%, and introduce five distinct feature extraction approaches, and study their combination using different ensemble methods.

翻译：本文提出了一种用于工业实体重识别的集成方法，以碎木托盘和镀锌金属板的图像作为数据集示例。我们的算法用简化基础模型的集成替代了常用的复杂孪生神经网络，在硬件受限场景下具有更广泛的适用性。每个集成子模型使用给定数据的不同类型提取特征作为输入，使得相比更复杂的现有模型，能在极短的训练时间内构建有效集成。我们在该任务中达到了当前最优性能，Rank-1准确率超过77%，Rank-10准确率超过99%，同时提出了五种特征提取方法，并研究了不同集成方法下的组合效果。