| dc.description.abstract | Barcodes are ubiguitous in modern life. Different types of barcodes are designed for different applications. Therefore, it is not easy to detect all types of barcodes using a single approach. In recent years, object detection in deep learning has achieved significant progresses, so this research aims to locate barcodes using deep learning.
In order to efficiently execute the system, which only needs to locate the barcode as a region of interest (ROI) without recognizing the type of each barcode, the simple and fast YOLOv3-tiny network has been chosen. Images used for training were captured by the professional scanner 1504P. The number of images were 10008 and then further divided into training data and verification data in 8:2.
The 133 data for testing were provided by CipherLab. The results of verification data and testing data shown that the recall could reach 95%, and the precision was 93% and 75%.
To implement the network in a resource-limited Personal Digital Assistant (PDA), we tried to prune the network, but the performance was not good. Hense we analyzed the network structure and used image processing techniques to imitate the network behavior. By visualizing the network, only coarse processing procedure could be identified. Finally, we tried to imitate some important feature maps with three methods. The first method searched for barcode candidates, located the centers of the barcodes using the 5 5 mask, and then used the active contour technique to frame up the barcode in an ROI. The second method was similar to the first method in finding barcode candidates and output ROI except using the smaller 3 3 mask to search for the center of barcodes in the middle step. The third method extended the second method with an additional processing stage, which filtered the ROI to reduce the number of erroneously detected areas. The recall and precision of three methods by testing data provided by CipherLab were evaluated. The results for these three methods were 83%, 92%, and 91% in the recall, and 81%、46% and 79% in the precision. The execution time of these three methods took 118ms ,84ms and 156ms in PDA, respectively. These three proposed methods were at similar recall and precision compared to other studies, but with significant improvement in running time. Our algorithms were competitive in execution speed compared to other approaches. | en_US |