Real-Time Detection of Unrecognized Objects in Logistics Warehouses Using Semantic Segmentation

Abstract

Pallet detection and tracking using computer vision is challenging due to the complexity of the object and its contents, lighting conditions, background clutter, and occlusions in industrial areas. Using semantic segmentation, this paper aims to detect pallets in a logistics warehouse. The proposed method examines changes in image segmentation from one frame to the next using semantic segmentation, taking into account the position and stationary behavior of newly introduced objects in the scene. The results indicate that the proposed method can detect pallets despite the complexity of the object and its contents. This demonstrates the utility of semantic segmentation for detecting unrecognized objects in real-world scenarios where a precise definition of the class cannot be given.