Wild Berry image dataset collected in Finnish forests and peatlands using drones

Berry picking has long-standing traditions in Finland, yet it is challenging and can potentially be dangerous. The integration of drones equipped with advanced imaging techniques represents a transformative leap forward, optimising harvests and promising sustainable practices. We propose WildBe, the first image dataset of wild berries captured in peatlands and under the canopy of Finnish forests using drones. Unlike previous and related datasets, WildBe includes new varieties of berries, such as bilberries, cloudberries, lingonberries, and crowberries, captured under severe light variations and in cluttered environments. WildBe features 3,516 images, including a total of 18,468 annotated bounding boxes. We carry out a comprehensive analysis of WildBe using six popular object detectors, assessing their effectiveness in berry detection across different forest regions and camera types. We will release WildBe publicly.

Object 6D pose estimation meets zero-shot learning

Object 6D pose estimation methods can achieve high accuracy when trained and tested on the same objects. However, estimating the pose of objects that are absent at training time is still a challenge. In this work, we advance the state-of-the-art in zero-shot object 6D pose estimation by proposing the first method that fuses the contribution of pre-trained geometric and vision foundation models. Unlike state-of-the-art approaches that train their pipeline on data specifically crafted for the 6D pose estimation task, our method does not require task-specific finetuning. Instead, our method, which we name PoMZ, combines geometric descriptors learned from point cloud data with visual features learned from large-scale web images to produce distinctive 3D point-level descriptors. By applying an off-the-shelf registration algorithm, like RANSAC, PoMZ outperforms all state-of-the-art zero-shot object 6D pose estimation approaches. We extensively evaluate PoMZ across the seven core datasets of the BOP Benchmark, encompassing over a hundred objects and 20 thousand images captured in diverse scenarios. PoMZ ranks first in the BOP Benchmark under the category Task 4: 6D localization of unseen objects. We will release the source code publicly.

The MONET dataset: Multimodal drone thermal dataset recorded in rural scenarios

We present MONET, a new multimodal dataset captured using a thermal camera mounted on a drone that flew over rural areas, and recorded human and vehicle activities. We captured MONET to study the problem of object localisation and behaviour understanding of targets undergoing large-scale variations and being recorded from different and moving viewpoints. Target activities occur in two different land sites, each with unique scene structures and cluttered backgrounds. MONET consists of approximately 53K images featuring 162K manually annotated bounding boxes. Each image is timestamp-aligned with drone metadata that includes information about attitudes, speed, altitude, and GPS coordinates. MONET is different from previous thermal drone datasets because it features multimodal data, including rural scenes captured with thermal cameras containing both person and vehicle targets, along with trajectory information and metadata. We assessed the difficulty of the dataset in terms of transfer learning between the two sites and evaluated nine object detection algorithms to identify the open challenges associated with this type of data. Project page: https://github.com/fabiopoiesi/monet_dataset.