In the computer vision, object detection has always been considered one of the most challenging issues because it requires classifying and locating objects in the same scene. Many object detection approaches were recently proposed based on deep convolutional neural networks (DCNNs), which have been demonstrated to achieve outstanding object detection performance compared to other approaches. However, the supervised training of DCNNs mostly uses gradient-based optimization criteria, in which all parameters of hidden layers require multiple iterations, and often faces some problems such as local minima, intensive human intervention, time-consuming, etc. In this paper, we propose a new method called Faster-YOLO, which is able to perform real-time object detection. The deep random kernel convolutional extreme learning machine (DRKCELM) and double hidden layer extreme learning machine auto-encoder (DLELM-AE) joint network is used as a feature extractor for object detection, which integrating the advantages of ELM-LRF and ELM-AE. It takes the raw images directly as input and thus is suitable for the different datasets. In addition, most connection weights are randomly generated, so there are few parameter settings and training speed is faster. The experiment results on Pascal VOC dataset show that Faster-YOLO improves the detection accuracy effectively by 1.1 percentage points compared to the original YOLOv2, and an average 2X speedup compared to YOLOv3.

在计算机视觉中，对象检测一直被认为是最具挑战性的问题之一，因为它需要在同一场景中对对象进行分类和定位。最近，基于深度卷积神经网络（DCNN）提出了许多目标检测方法，与其他方法相比，这些方法已证明可实现出色的目标检测性能。但是，DCNN的监督训练主要使用基于梯度的优化标准，其中隐藏层的所有参数都需要多次迭代，并且经常会遇到一些问题，例如局部极小值，人工干预，费时等。我们提出了一种称为Faster-YOLO的新方法，该方法能够执行实时对象检测。深度随机核卷积极限学习机（DRKCELM）和双层隐藏层极限学习机自动编码器（DLELM-AE）联合网络被用作目标检测的特征提取器，它们融合了ELM-LRF和ELM-AE的优势。它直接将原始图像作为输入，因此适用于不同的数据集。另外，大多数连接权重是随机生成的，因此参数设置很少，训练速度更快。在Pascal VOC数据集上的实验结果表明，Faster-YOLO与原始YOLOv2相比有效地提高了检测精度1.1个百分点，与YOLOv3相比平均提高了2倍。整合了ELM-LRF和ELM-AE的优势。它直接将原始图像作为输入，因此适用于不同的数据集。另外，大多数连接权重是随机生成的，因此参数设置很少，训练速度更快。在Pascal VOC数据集上的实验结果表明，Faster-YOLO与原始YOLOv2相比有效地提高了检测精度1.1个百分点，与YOLOv3相比平均提高了2倍。整合了ELM-LRF和ELM-AE的优势。它直接将原始图像作为输入，因此适用于不同的数据集。另外，大多数连接权重是随机生成的，因此参数设置很少，训练速度更快。在Pascal VOC数据集上的实验结果表明，Faster-YOLO与原始YOLOv2相比，有效地提高了检测精度1.1个百分点，与YOLOv3相比，平均提高了2倍。