| Abstract:In view of the current X-ray image security inspection method for dangerous goods identification, which does not collect fuzzy static image targets, resulting in poor security inspection of dangerous goods images, low recognition rate of dangerous goods, and long recognition time, an automatic identification method of dangerous good in X-ray image security inspection based on VR technology is proposed. Obtaining security inspection dangerous goods imaging through X-rays, using VR technology to acquire fuzzy static image targets, using optical imaging principles to process fuzzy static image targets in layers, acquiring fuzzy static image targets' brightness layer and detail layer, and compressing fuzzy static image targets for adaptive partitioning, realizing the target reproduction of dangerous goods images. Extracting the texture features of dangerous goods images based on the Walsh transform method, building a BP neural network model, repeatedly adjusting the weights and thresholds and perform training to ensure that the output error is minimized, and realize the X-ray image security inspection of dangerous goods auto recognition. The experimental results show that the proposed method has a better presentation effect of dangerous goods images for security inspection, can effectively improve the recognition rate of dangerous goods, shorten the time of dangerous goods recognition, and have good dangerous goods recognition performance. |
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