سه شنبه 18 اردیبهشت 1403
| عنوان مقاله | |
| عنوان مقاله |
A unified multi-scale deep convolutional neural network for fast object detection |
| عنوان فارسی مقاله | یک شبکه عصبی کانونی عصبی چند مقیاس متحد برای شناسایی سریع شی |
| مشخصات مقاله انگلیسی | |
| نشریه: Springer |
|
| سال انتشار |
2016 |
| عنوان مجله |
European Conference on Computer Vision |
| تعداد صفحات مقاله انگلیسی | 17 |
| رفرنس | دارد |
| تعداد رفرنس | 43 |
| چکیده مقاله | |
| چکیده |
A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast multi-scale object detection. The MS-CNN consists of a proposal sub-network and a detection subnetwork. In the proposal sub-network, detection is performed at multiple output layers, so that receptive fields match objects of different scales. These complementary scale-specific detectors are combined to produce a strong multi-scale object detector. The unified network is learned endto-end, by optimizing a multi-task loss. Feature upsampling by deconvolution is also explored, as an alternative to input upsampling, to reduce the memory and computation costs. State-of-the-art object detection performance, at up to 15 fps, is reported on datasets, such as KITTI and Caltech, containing a substantial number of small objects. |
| کلمات کلیدی |
|
| دانلود | |
