Towards smarter warehouse perception: integrating adaptive tile segmentation in warehousing object detection pipelines

• Warehousing environments pose challenges for accurate object detection. • State-of-the-art models struggle to locate regions of interest in complex domains. • Zero-shot segmentation aids in region identification and improves detection accuracy. • Adaptive Tiles improves object detection in intralogistics scenarios. Traditional object detection methods struggle in complex logistics and warehousing environments due to their inability to effectively identify regions of interest while handling extreme occlusion, high object density, and significant scale variations with static. Existing region proposal approaches (anchor-based, anchor-free, and deep learning-based) require meticulous hyperparameter tuning, struggle with small or overlapping objects, have difficulties with different lighting conditions or suffer from poor localization in cluttered scenes. We introduce Adaptive Tiles , a novel preprocessing algorithm that dynamically identifies potential object regions prior to detection, thereby addressing these limitations. The core mechanism involves a three-step process: (1) zero-shot segmentation for adaptable, object-agnostic mask generation, (2) filter heuristics to refine masks into robust region proposals, and (3) bounding box combination for merging proposals into optimal detection zones. By shifting from static region identification methods to a dynamic, segmentation-driven proposal mechanism, Adaptive Tiles significantly enhances the efficiency and accuracy of object detectors, as demonstrated by its integration into the DMZoomNet framework and evaluation on the challenging LOCO dataset.