| Title |
Image Target Detection Algorithm of Smart City Management Cases |
| ID_Doc |
36340 |
| Authors |
Tan, P; Mao, KD; Zhou, S |
| Title |
Image Target Detection Algorithm of Smart City Management Cases |
| Year |
2020 |
| Published |
|
| DOI |
10.1109/ACCESS.2020.3021248 |
| Abstract |
With the rapid development and wide application of Internet technology, the use of the concept of "smart city" has been concerned and promoted. It is an inevitable way to strengthen the breadth and depth of urban services, and to move forward from digital to intelligent application. The purpose of this study is to further promote the small and medium-sized cities in China to improve the "smart city" management mode and promote the harmonious development of society, which has important reference and practical significance. In this paper, in-depth analysis of the background of smart city, different image target detection algorithms are studied. The infrared target detection algorithm suppresses the background by means of a high-pass filter, and the coefficient of correlation between the characteristics is used as the fusion weight, while the weighted grey synthesis is performed, area and seroid offset. The ultra-spectral target detection algorithm extracts some content indicators from the initial data, and finally realizes the optimization of the algorithm. The mean filtering algorithm can reduce the effect of noise by pre-processing the image. The algorithm a hog-target detection describes the features of the object's surface edges in areas such as graphics and image processing; and calculates the distribution of characteristics in the direction of inclination of the particular part of the image. These algorithms have their own advantages and characteristics. The results of the experiment show that the accuracy and rate of recall of the infrared target detection algorithm after aggregation of characteristics are higher than other algorithms, the accuracy is higher than 6.3% of the original infrared image algorithm, and the recall rate is 5.4% higher than the infrared image anide algorithm. The change in the value m of the main vector dimension will affect the accuracy of target detection. |
| Author Keywords |
Object detection; Smart cities; Feature extraction; Filtering algorithms; Classification algorithms; Hyperspectral imaging; Smart city; target detection; algorithm research; accuracy comparison |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000572983300001 |
| WoS Category |
Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications |
| Research Area |
Computer Science; Engineering; Telecommunications |
| PDF |
https://ieeexplore.ieee.org/ielx7/6287639/8948470/09184937.pdf
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