Research highlights: An automatic localization system for ground obstacles on harvested forest land based on existing mature hardware and software architecture has been successfully implemented. In the tested area, 98% of objects were successfully detected and could on average be positioned within 0.33 m from their true position in the full range 1–10 m from the camera sensor. Background and objectives: Forestry operations in forest environments are full of challenges; detection and localization of objects in complex forest terrains often require a lot of patience and energy from operators. Successful automatic real-time detection and localization of terrain objects not only can reduce the difficulty for operators but are essential for the automation of harvesting and logging tasks. We intend to implement a system prototype that can automatically locate ground obstacles on harvested forest land based on accessible hardware and common software infrastructure. Materials and Methods: An automatic object detection and localization system based on stereo camera sensing is described and evaluated in this paper. This demonstrated system detects and locates objects of interest automatically utilizing the YOLO (You Only Look Once) object detection algorithm and derivation of object positions in 3D space. System performance is evaluated by comparing the automatic detection results of the tests to manual labeling and positioning results. Results: Results show high reliability of the system for automatic detection and location of stumps and large stones and shows good potential for practical application. Overall, object detection on test tracks was 98% successful, and positional location errors were on average 0.33 m in the full range from 1–10 m from the camera sensor. Conclusions: The results indicate that object detection and localization can be used for better operator assessment of surroundings, as well as input to control machines and equipment for object avoidance or targeting.

To further develop forest production, higher automation of forest operations is required. Such endeavour promotes research on unmanned forest machines. Designing unmanned forest machines that exercise forwarding requires an understanding of positioning and angle estimations of logs after cutting and delimbing have been conducted, as support for subsequent crane loading work. This study aims to improve the automation of the forwarding operation and presents a system to realize real-time automatic detection, positioning, and angle estimation of harvested logs implemented on an existing unmanned forest machine experimental platform from the AORO (Arctic Off-Road Robotics) Lab. This system uses ROS as the underlying software architecture and a Zed2 camera and NVIDIA JETSON AGX XAVIER as the imaging sensor and computing platform, respectively, utilizing the YOLOv3 algorithm for real-time object detection. Moreover, the study combines the processing of depth data and depth to spatial transform to realize the calculation of the relative location of the target log related to the camera. On this basis, the angle estimation of the target log is further realized by image processing and color analysis. Finally, the absolute position and log angles are determined by the spatial coordinate transformation of the relative position data. This system was tested and validated using a pre-trained log detector for birch with a mean average precision (mAP) of 80.51%. Log positioning mean error did not exceed 0.27 m and the angle estimation mean error was less than 3 degrees during the tests. This log pose estimation method could encompass one important part of automated forwarding operations.

Research on automated seedling planting systems in forestry is a crucial aspect of forestry automation. This paper introduces the development of a vision-based automated seedling planting position selection system, integrated with hardware and software components on an unmanned forest machine platform. Developed around object detection as the core, this research presents a comprehensive system consisting of two main functionalities: (i) A vision system that performs obstacle detection and localization, providing estimated obstacle types, sizes, and positions to the plant planner function. (ii) A plant planner function utilizes this information to plan the plantable areas and selects suitable planting locations. The integrated system has been tested in the field and we found it to effectively determine suitable planting locations on the ground of a clear-cut. The implementation of this system lays the foundation for subsequent automated planting operations. Furthermore, the automation of forest seedling planting reduces the need for manual labor and enhances planting precision, contributing to improved forest health and ecological balance. Looking ahead, this research offers insights into the future development of unmanned forestry operations, making strides in automating forest management, achieving cost-effectiveness, and facilitating ecological restoration. 

At present, there is extensive ongoing research and development on autonomous road vehicles, but much less effort is put on autonomous driving in off-road environments, especially with regard to autonomous machines driving in forest terrain. At the same time, forest machine work is harsh for the operator, and the operator’s maximum performance is a major bottleneck for machine operations. One way to enable a safe work environment is to remove the driver from the machine. Thus, forestry machines must become autonomous in order to improve personnel safety and work efficiency. The basis of enabling automation of forest machinery is highly precise and accurate acquisition of working environment information, especially reliable virtual representation of forest terrain and obstacles within. In this paper, the objective is to improve the accuracy of object detection in forest terrain based on an AI algorithm by adding the depth variation texture to corresponding area of  RGB images. The results show that the algorithm significantly improves the accuracy of object detection while the probability of false detection is reduced. Meanwhile, computational efficiency compared to previous image processing is not reduced to such extent that it prevents real-time computation.