Implementation of a System for Real-Time Detection and Localization of Terrain Objects on Harvested Forest Land

Abstract

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.

Exploring the feasibility of autonomous forestry operations: Results from the first experimental unmanned machine

“…At the time for full testing, YOLOv3 had shown substantial improvement compared with earlier versions (Redmon & Farhadi, 2018) and had ROS integration. The detector was previously tested in forestry applications (Li & Lideskog, 2021).…”

Instance Segmentation for Autonomous Log Grasping in Forestry Operations

“…Silva et al [25] published a dataset of standing trees for bounding boxes detection and trained a YOLOv3 [26] network on it. Li et al [27] trained this same network for detecting ground obstacles in a harvested forest, on a custom dataset. Nevertheless, the latter two datasets only provide bounding boxes ground truths, which can be useful to visually assist the machine operator, but hardly applicable to precise localization.…”

Unimodal and Multimodal Perception for Forest Management: Review and Dataset

“…The system was tested in a simulated and in a real environment: in the simulated environment, the UAV concluded 85% of the test flights without collisions, and in the real environment, the UAV concluded all test flights without collisions. Other studies focused on detecting tree trunks in street images using Deep Learning methods [29,30], in dense forests using visible and thermal imagery combined with Deep Learning [31], and even on the detection of stumps in harvested forests [32] to enhance the surrounding awareness of the operators and to endow machines with intelligent object avoidance systems.…”

“…Health and diseases Offline [4-8] Inventory and structure Offline [9-23] Navigation Online [24][25][26][27][28][29][30][31][32] The aim of the "Health and diseases" category is to monitor the health of forest lands and detect the existence of diseases that affect forest trees, destroying some forest cultures and ecosystems. Data from this category are most of the times processed offline-the data are collected in the field and are processed later.…”