Enhancing Battery Detection in X-Ray Imaging in WEEE with a 16 bit Deep Learning Pipeline

By detecting internal batteries before treatment, the end-of-life handling of Waste Electrical and Electronic Equipment (WEEE) can be made more safe and cost efficient. Where prior research has demonstrated the potential of deep learning models to detect batteries in X-ray transmission (XRT) images of WEEE, these studies did not fully investigate the potential of the higher bit depth that XRT machines are capable of. A 16 bit bit-depth enables the representation of 65,536 shades in greyscale, unlike the 8 bit bit-depth that is commonly used in deep learning approaches, which is limited to 256 shades. Therefore, the presented research investigates the impact of higher bit-depth on the performance of battery detection in WEEE. In addition, the detection and classification performance of batteries is tested across two sets of X-ray source parameters (120kV and 100mA; 60kV and 40mA). Results show that, for this use case with the same set of source parameters, using 16 bit image data yields small but consistent improvement in overall mAP50 and mAP50-95 as compared to using 8 bit image data. Hence, the findings of the presented research contribute to advancing WEEE recycling processes through improved battery detection in XRT images, contributing to more efficient resource recovery.