Abstract
In recent years, neural networks with an additional convolutional layer, referred to as convolutional neural networks (CNN), have widely been recognized as being effective in the field of image recognition. In the majority of these previous researches, the structures of networks were designed by hand, and were based on experience. However, there is no established theory explaining how to build networks with higher learning abilities. In this chapter, we propose a framework on automatically obtaining network structures with the highest learning ability for image recognition, through the combination of the various core technologies. We employ EC (evolutionary computation) for the automatic extraction and synthesis of network structures. Additionally, we attempt to perform an effective search in a larger parameter space by gradually increasing the number of training epochs during the generation change process. In order to show the effectiveness of our approach, we apply the proposed method to the task of detecting dangerous objects in an X-ray image data set. Compared with the previous results, we have achieved an improvement in the mAP value. We can also find several by-passes in the structures that were actually obtained.
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- 1.
Instead of multi-view X-ray devices which are expensive and heavy, we use a portable X-ray device (see Fig. 12.10). While multi-view devices need to colorize images, our device can collect single-view X-ray images in real time.
- 2.
More precisely, the actual images to be used for training and testing are randomly changed each time and the system is run 20 times from Run 1 up to Run 20. Here, we used the set of images corresponding to Run 11, whose results are the most representative of the average.
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Acknowledgements
We would like to thank Shin Yokoshima and Yoji Nikaido, T&S Corporation, for providing us with X-ray data collection environment.
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Tsukada, R., Zou, L., Iba, H. (2020). Evolving Deep Neural Networks for X-ray Based Detection of Dangerous Objects. In: Iba, H., Noman, N. (eds) Deep Neural Evolution. Natural Computing Series. Springer, Singapore. https://doi.org/10.1007/978-981-15-3685-4_12
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