DF-SSD: An Improved SSD Object Detection Algorithm Based on DenseNet and Feature Fusion

Enhancement of SSD by concatenating feature maps for object detection

We propose an object detection method that improves the accuracy of the conventional SSD (Single Shot Multibox Detector), which is one of the top object detection algorithms in both aspects of accuracy and speed. The performance of a deep network is known to be improved as the number of feature maps increases. However, it is difficult to improve the performance by simply raising the number of f...

Estimating uncertainty in SSD-based feature tracking

SSD-based feature trackers have enjoyed growing popularity in recent years, particularly in the field of visual servo control of robotic manipulators. These trackers use sum-ofsquared-differences correlation measures to locate target features in sequences of images. The results can then be used to estimate the motion of objects in the scene, to infer the 3D structure of the scene, or to control...

Feature-Fused SSD: Fast Detection for Small Objects

Small objects detection is a challenging task in computer vision due to its limited resolution and information. In order to solve this problem, the majority of existing methods sacrifice speed for improvement in accuracy. In this paper, we aim to detect small objects at a fast speed, using the best object detector Single Shot Multibox Detector (SSD) with respect to accuracy-vs-speed trade-off a...

Estimating Un ertainty in SSD-Based Feature Tra king

An Analysis on Empirical Performance of SSD-Based RAID

In this paper, we measure the I/O performance of five filesystems— EXT4, XFS, BTRFS, NILFS2, and F2FS, with five storage configurations— single SSD, RAID 0, RAID 1, RAID 10, and RAID 5. We show that F2FS on RAID 0 and RAID 5 with eight SSDs outperforms EXT4 by 5 times and 50 times, respectively. We also make a case that RAID controller can be a significant bottleneck in building a RAID system w...