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首页> 外文会议>2017 International Conference on Security, Pattern Analysis, and Cybernetics >Improving object detection accuracy with region and regression based deep CNNs
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Improving object detection accuracy with region and regression based deep CNNs

机译：通过基于区域和回归的深度CNN来提高物体检测的准确性

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摘要

Object detection has made great improvements in convolutional neural networks (CNNs), which is the high-capacity visual model that yields hierarchies of discriminative features. Object detection based on CNNs is in general divided into two aspects: region based detection and regression based detection. In this paper, we aim at further advancing object detection performance by properly utilizing the complementary results of those two streams. By investigating errors of several previous state-of-the-art methods about the two streams, we discover that those detection results of two general streams are complementary in object recognition and localization. Region based methods achieve high recall but simultaneously struggle with localization problems, while regression based methods make less localization errors by iteratively regressing the object to target localization. Driven by these observations, we propose two kinds of fusion paradigms to combine the results of those two streams. One is direct fusion utilizing the complementary results of those two streams and adopting non-maximal suppression (NMS) and voting operation to make full use of the results generated by two streams. In addition, considering direct fusion may compromise the original performance of object detections, we also propose another method, modifies voting operation that just refines the box coordinate without having any other impact on the original detections and further boosts the performance by an adding operation. Extensive experiments show that our two ensemble paradigms both boost the state-of-the-art results on Pascal VOC dataset.
机译：目标检测在卷积神经网络（CNN）中取得了很大的进步，卷积神经网络是高容量视觉模型，可产生区分特征的层次结构。基于CNN的对象检测通常分为两个方面：基于区域的检测和基于回归的检测。在本文中，我们旨在通过适当利用这两个流的互补结果来进一步提高对象检测性能。通过研究关于这两个流的几种现有的最新技术方法的错误，我们发现两个常规流的检测结果在对象识别和定位方面是互补的。基于区域的方法实现了较高的查全率，但同时又遇到了定位问题，而基于回归的方法则通过迭代地将对象回归到目标定位而减少了定位错误。基于这些观察，我们提出了两种融合范式来组合这两个流的结果。一种是直接融合，利用这两个流的互补结果，并采用非最大抑制（NMS）和表决操作来充分利用两个流生成的结果。此外，考虑到直接融合可能会损害对象检测的原始性能，我们还提出了另一种方法，修改投票操作，该操作仅细化框坐标，而对原始检测没有任何其他影响，并且通过添加操作进一步提高了性能。大量的实验表明，我们的两个集成范例都可以提高Pascal VOC数据集的最新结果。

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