Object recognition systems are usually trained and evaluated on high resolution images. However, in real world applications, it is common that the images have low resolutions or have small sizes. In this study, we first track the performance of the state-of-the-art deep object recognition network, Faster- RCNN, as a function of image resolution. The results reveals negative effects of low resolution images on recognition performance. They also show that different spatial frequencies convey different information about the objects in recognition process. It means multi-resolution recognition system can provides better insight into optimal selection of features that results in better recognition of objects. This is similar to the mechanisms of the human visual systems that are able to implement multi-scale representation of a visual scene simultaneously. Then, we propose a multi-resolution object recognition framework rather than a single-resolution network. The proposed framework is evaluated on the PASCAL VOC2007 database. The experimental results show the performance of our adapted multi-resolution Faster-RCNN framework outperforms the single-resolution Faster-RCNN on input images with various resolutions with an increase in the mean Average Precision (mAP) of 9.14% across all resolutions and 1.2% on the full-spectrum images. Furthermore, the proposed model yields robustness of the performance over a wide range of spatial frequencies.

翻译：目标识别系统通常在高质量分辨率图像上进行训练和评估。然而在实际应用中，图像常出现低分辨率或尺寸较小的情况。本研究首先追踪了最先进的深度目标识别网络Faster-RCNN在不同图像分辨率下的性能表现。结果表明低分辨率图像会对识别性能产生负面影响，同时揭示了不同空间频率在识别过程中传递的物体信息存在差异。这意味着多分辨率识别系统能更优地理解特征选择的最优化方案，从而提升目标识别效果。这与人类视觉系统能够同时实现视觉场景多尺度表征的机制类似。随后，本文提出了一种多分辨率目标识别框架，而非单一分辨率网络。该框架在PASCAL VOC2007数据库上进行评估。实验结果表明，所提出的自适应多分辨率Faster-RCNN框架在多种分辨率输入图像上的性能均优于单一分辨率Faster-RCNN，其平均精度均值(mAP)在所有分辨率下提升9.14%，在全频谱图像上提升1.2%。此外，所提模型在宽空间频率范围内展现出稳健的性能表现。