Name
Abstract | ||
|---|---|---|
To facilitate the management of detonators, an automatic spotting system is proposed for detonator coded characters based on convolutional neural networks. The system contains a multi-scale detection network, a multi-label recognition network and a post-processing layer. An improved fully convolution network (FCN) is designed as the multi-scale detection network to obtain the accurate response map of the detonator image. Two subnetworks are parallel integrated into the FCN to perform a coarse-to-fine detection. An improved Jaccard loss function with a regularization term is defined to train the FCN. The region of interest (ROI) of the detonator image is achieved when the response map is post-processed by the post-processing layer. Finally, a modified multi-label network is used to recognize the detonator coded characters in the ROI. The experimental results indicate that the proposed system achieves a better spotting performance for detonator coded characters than the state-of-the-art text spotting methods in terms of accuracy and efficiency. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11760-019-01525-1 | Signal, Image and Video Processing |
Keywords | Field | DocType |
Detonator coded character, Text spotting, Convolutional neural network, Fully convolutional network, Jaccard loss function | Computer vision,Pattern recognition,Convolution,Convolutional neural network,Detonator,Regularization (mathematics),Artificial intelligence,Jaccard index,Region of interest,Spotting,Mathematics | Journal |
Volume | Issue | ISSN |
14 | 1 | 1863-1703 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guandong Cen | 1 | 4 | 1.80 |
| Nian Cai | 2 | 28 | 6.74 |
| Jixiu Wu | 3 | 6 | 1.21 |
| Feiyang Li | 4 | 2 | 1.44 |
| Han Wang | 5 | 1 | 2.04 |
| Guotian Wang | 6 | 0 | 0.34 |