Optimising the Computer Vision Module of Eonics’ Autonomous Drone

Abstract

Items being misplaced in warehouses easily get lost. To combat this, warehouses have to send people in scanning all barcodes in the warehouse. This is highly inefficient, which is why Eonics wants to build a drone handling this. There are options out there to scan barcodes, but none of them match the requirements laid out by Eonics. Among these requirements are a lightweight camera, such as a GoPro, and a recording distance of 1.5-2 metres. This report will look and see if these requirements are feasible. Techniques used in this report are Mathematical Morphology, Maximally Stable Extremal Regions, Convolutional Neural Networks, Gradiental Difference and Direction Estimation with Region Extraction. The report concludes in stating that interpreting the barcodes is not possible with mere software under these requirements. The maximal distance we were able to interpret barcodes from, based on a 4K image, was around 1 metre. Continuing the trend, we would need at least an 8K camera to detect from a distance of 1.5 metres. Detection however, is less difficult and is feasible from a distance of 1.5-2 metres. The report also derives an function to use to calculate the maximum distance a barcode can be interpreted from, based on the details of the barcode and camera. Finally, research is done regarding using hardware solutions, such as a zoom-lens, which has promising results.