In the field of object detection, recently, tremendous success is achieved, but still it is a very challenging task to detect and identify objects accurately with fast speed. Human beings can detect and recognize multiple objects in images or videos with ease regardless of the object’s appearance, but for computers it is challenging to identify and distinguish between things. In this paper, a modified YOLOv1 based neural network is proposed for object detection. The new neural network model has been improved in the following ways. Firstly, modification is made to the loss function of the YOLOv1 network. The improved model replaces the margin style with proportion style. Compared to the old loss function, the new is more flexible and more reasonable in optimizing the network error. Secondly, a spatial pyramid pooling layer is added; thirdly, an inception model with a convolution kernel of 1 1 is added, which reduced the number of weight parameters of the layers. Extensive experiments on Pascal VOC datasets 2007/2012 showed that the proposed method achieved better performance.

中文翻译：

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通过改进的 YOLO 神经网络进行目标检测

在物体检测领域，最近取得了巨大的成功，但快速准确地检测和识别物体仍然是一项非常具有挑战性的任务。无论物体的外观如何，人类都可以轻松检测和识别图像或视频中的多个物体，但对于计算机而言，识别和区分事物具有挑战性。在本文中，提出了一种改进的基于 YOLOv1 的神经网络用于目标检测。新的神经网络模型在以下方面进行了改进。首先对YOLOv1网络的损失函数进行修改。改进后的模型将边距样式替换为比例样式。与旧的损失函数相比，新的在优化网络误差方面更灵活、更合理。其次，增加了一个空间金字塔池化层；第三，添加了卷积核为1 1的inception模型，减少了层的权重参数数量。在 Pascal VOC 数据集 2007/2012 上的大量实验表明，所提出的方法取得了更好的性能。