2D Barcode Detection using Images for Drone-assisted Inventory Management

Drone-assisted inventory management is attractive for companies with large warehouses and factories. Additionally, there is an interest in a novel method that automatically detects target barcodes using a IR-based camera, which enables efficient drone path planning and results in reducing power consumption. In this paper, we propose an efficient detection framework which determines the localizations of 2D barcodes. Many regional proposals of 2D barcodes are reduced to a few candidate regions according to the distance information between the drone and the target 2D barcode. Visual features of the selected candidate regions are extracted by LBP and HOG methods, respectively. To gain discriminant power for classification, SVM is used at the end of the procedure. The final detection region is determined by a weighted sum-based score fusion method. To validate the performance of the proposed method, we collect 2D barcode images under real-life warehouse conditions and obtain extensive experiment results.