Abstract
With the soaring popularity of electronic gadgets, Lithium-Ion Batteries (LIB) have witnessed a remarkable surge. The inspiration behind this study arises from the urgent need to automate the identification of batteries in diverse contexts, such as electronic waste recycling facilities or security screening at airports. Ultimately, it strives to minimize health hazards associated with battery recycling by enabling more accurate sorting with minimal human involvement. In this paper, we applied double transfer learning to eight cutting-edge object detectors, unlocking the potential of X-Ray images in recognizing and categorizing electronic mobile devices (EMD) along with their embedded Lithium-Ion batteries (LIB).
D. Rohrschneider and N.A. Baker—Equal contribution.
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Rohrschneider, D., Baker, N.A., Handmann, U. (2023). Double Transfer Learning to Detect Lithium-Ion Batteries on X-Ray Images. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14134. Springer, Cham. https://doi.org/10.1007/978-3-031-43085-5_14
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