Deep Neural Networks for Object Detection

Deep Neural Networks for Object DetectionChristian Szegedy Alexander Toshev Dumitru ErhanGoogle, Inc.{szegedy, toshev, Neural Networks (DNNs) have recently shown outstanding performance onimage classification tasks [14]. In this paper we go one step further and addressthe problem of Object Detection using DNNs, that is not only classifying but alsoprecisely localizing objects of various classes. We present a simple and yet pow-erful formulation of Object Detection as a regression problem to Object boundingbox masks. We define a multi-scale inference procedure which is able to pro-duce high-resolution Object detections at a low cost by a few network performance of the approach is shown on Pascal IntroductionAs we move towards more complete image understanding, having more precise and detailed objectrecognition becomes crucial. In this context, one cares not only about classifying images, but alsoabout precisely estimating estimating the class and location of objects contained within the images,a problem known as Object main advances in Object Detection were achieved thanks to improvements in Object representa-tions and machine learning models.}

Figure 1: A schematic view of object detection as DNN-based regression. DNN DNN object box extraction object box extraction reÞne scale 1 scale 2 small set of boxes covering image merged object masks Figure 2: After regressing to object masks across several scales and large image boxes, we perform object box extraction.

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  Object, Detection, Schematics, Object detection

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