Name
Abstract | ||
|---|---|---|
With the development of computer technology, the related achievements of image processing have been applied. Among them, the results of automatic target detection and recognition are widely used in the fields of reconnaissance, early warning and traffic control with the application of UAV. But now, the research of automatic target detection and tracking is becoming smaller and smaller. The original automatic target detection and recognition algorithm seems to be inadequate. The bottleneck of low-level feature design and optimization makes the accuracy and efficiency of automatic target detection inefficient. Therefore, based on in-depth learning, this paper establishes a method to automatically learn effective image features from images to achieve automatic target detection. Through the simulation of target detection in VEDAI database. The results show that the recognition rate of the proposed model is more than 95%. The results show that the proposed method can realize the automatic detection and recognition of targets very well. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.jvcir.2019.01.017 | Journal of Visual Communication and Image Representation |
Keywords | Field | DocType |
Image processing,Target detection,Target recognition,In-depth learning | Warning system,Computer vision,Bottleneck,Automatic target detection,Pattern recognition,Feature (computer vision),Image processing,Artificial intelligence,Deep learning,Mathematics,Feature design,Computer technology | Journal |
Volume | ISSN | Citations |
60 | 1047-3203 | 1 |
PageRank | References | Authors |
0.37 | 18 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jia Wang | 1 | 79 | 17.75 |
| Chen Liu | 2 | 34 | 26.22 |
| Tian Fu | 3 | 1 | 0.37 |
| Lili Zheng | 4 | 67 | 8.88 |