【Background】：ECCV is one of the top conferences in computer vision,In this blog,I will introduce an paper from Sun Jian team, which is about a backbone network for object detection.What is worth mentioning is that this paper does not have any formula.

paper name: DetNet: A Backbone network for Object Detection
paper url: https://arxiv.org/abs/1804.06215

paper-information

1、【Abstract】

Object detection is a heavily researched topic in computer vision. It aims at finding “where” and “what” each object instance is when given an image.

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2、【Motivation】

the existing backbone networks usually have some problems,because they are desighed for classification task at first,there is no doubt that there are differences between different tasks(classification and detection).so,de signing a new backbone networks for detection is become very neccessary.

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3、【The problems of exist backbone network】

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4、【Comparisons of different backbones used in FPN】

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5、【Contributions】

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6、【DetNet design】

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7、【More details】

the author adopt ResNet-50 as baseline, which is widely used as the backbone network in a lot of object detectors. To fairly compare with the ResNet-50, we keep stage 1,2,3,4 the same as original ResNet-50 for our DetNet.the more details you can see picture 7.png.

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apply bottleneck with dilation as a basic network block to efficiently enlarge the receptive filed. Since dilated convolution is still time consuming,our stage 5 and stage 6 keep the same channels as stage 4 (256 input channels for bottleneck block). This is different from traditional backbone design,which will double channels in a later stage.

More information about dilation:https://blog.csdn.net/jzrita/article/details/72639969

8、【Experiment】

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