Woodlog Inventory Optimization using Object Detection and Object Tracking

Abstract

In the timber industry, keeping track of wood stacks is crucial to avoid stockpiles that are too large or too small ensuring efficient operations. Traditional methods are often time-consuming and inaccurate, leading to inefficiencies. This research proposes a novel solution for timber yard inventory management using cutting-edge technology.The approach utilizes YOLOv8, a powerful object detection algorithm, to identify wood logs in live video feeds. DeepSORT, a tracking algorithm, then follows these identified logs over time. This automation eliminates the need for manual tracking, minimizes errors, and enforces the "First-In-First-Out" (FIFO) principle for efficient inventory use.The study also compares DeepSORT to other tracking algorithms like OC-SORT and ByteTrack to identify the most effective option for this specific application. The results demonstrate that the proposed method significantly improves detection accuracy and reliability, leading to better inventory management.In essence, this research highlights how integrating advanced detection and tracking technologies can revolutionize timber yard operations. By automating processes and ensuring accurate inventory control, these technologies can significantly reduce costs and boost overall efficiency in the timber sector.