Instance Segmentation with a Novel Tree Log Detection Dataset

Reliable tree log detection is a key requirement for automation of forestry operations. Despite the substantial progress regarding object detection in general, tree log detection lags behind due to the lack of well-annotated datasets. In order to address this gap, we introduce the Tree Log Detection Dataset (TLDD). This real-world dataset is collected using a combination of 360 \(^\circ \) multiline LIDAR and stereo cameras. It offers a wide range of annotated segmentation masks for over 1000 images of about 22000 tree logs. We assess the quality of the presented data set by comparing it to existing data sets using state-of-the-art architectures such as MaskDINO and Mask2Former. Our experiments demonstrate the quality of TLDD and confirm the efficiency of attention-based, transformer-like networks.