Name
Abstract | ||
|---|---|---|
Vision-based object detection is essential for a multitude of robotic applications. However, it is also a challenging job due to the diversity of the environments in which such applications are required to operate, and the strict constraints that apply to many robot systems in terms of run-time, power and space. To meet these special requirements of robotic applications, we propose an efficient deep network for vision-based object detection. More specifically, for a given image captured by a robot mount camera, we first introduce a novel proposal layer to efficiently generate potential object bounding-boxes. The proposal layer consists of efficient on-line convolutions and effective off-line optimization. Afterwards, we construct a robust detection layer which contains a multiple population genetic algorithm-based convolutional neural network (MPGA-based CNN) module and a TLD-based multi-frame fusion procedure. Unlike most deep learning based approaches, which rely on GPU, all of the on-line processes in our system are able to run efficiently without GPU support. We perform several experiments to validate each component of our proposed object detection approach and compare the approach with some recently published state-of-the-art object detection algorithms on widely used datasets. The experimental results demonstrate that the proposed network exhibits high efficiency and robustness in object detection tasks. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.neucom.2017.03.050 | Neurocomputing |
Keywords | Field | DocType |
Deep network,Object detection,Computer vision,Robotic application,MPGA | Computer vision,Object detection,Population,Viola–Jones object detection framework,Convolutional neural network,Computer science,Robustness (computer science),Artificial intelligence,Deep learning,Robot,Genetic algorithm,Machine learning | Journal |
Volume | Issue | ISSN |
245 | C | 0925-2312 |
Citations | PageRank | References |
3 | 0.37 | 33 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keyu Lu | 1 | 16 | 4.09 |
| Xiangjing An | 2 | 226 | 12.15 |
| Jian Li | 3 | 49 | 6.61 |
| Hangen He | 4 | 307 | 23.86 |