论文标题
使用转移学习和单次通过深度学习体系结构来构建强大的工业适用对象检测模型
Building Robust Industrial Applicable Object Detection Models Using Transfer Learning and Single Pass Deep Learning Architectures
论文作者
论文摘要
在计算机视觉和人工智能中深度学习的起义趋势根本不容忽视。从识别和检测到细分的最多样化的任务上，深度学习能够获得最新的结果，达到一流的表现。在本文中，我们探讨了使用最先进的开源深度学习框架（如Darknet），探讨了专门针对对象检测任务的深卷卷积神经网络如何改善我们面向工业的对象检测管道。通过使用一个深度学习架构，该体系结构在一次运行中集成了区域建议，分类和概率估计，我们旨在获得实时性能。我们专注于通过探索转移学习来大大减少所需的培训数据，同时仍保持高平均精度。此外，我们将这些算法应用于两个与工业相关的应用程序，一个是对眼睛跟踪数据中的促销板的检测，另一个是检测和识别仓库产品包装和识别用于增强广告的包装。
The uprising trend of deep learning in computer vision and artificial intelligence can simply not be ignored. On the most diverse tasks, from recognition and detection to segmentation, deep learning is able to obtain state-of-the-art results, reaching top notch performance. In this paper we explore how deep convolutional neural networks dedicated to the task of object detection can improve our industrial-oriented object detection pipelines, using state-of-the-art open source deep learning frameworks, like Darknet. By using a deep learning architecture that integrates region proposals, classification and probability estimation in a single run, we aim at obtaining real-time performance. We focus on reducing the needed amount of training data drastically by exploring transfer learning, while still maintaining a high average precision. Furthermore we apply these algorithms to two industrially relevant applications, one being the detection of promotion boards in eye tracking data and the other detecting and recognizing packages of warehouse products for augmented advertisements.
