Automatic counting of mounds on UAV images: combining instance segmentation and patch-level correction

TL;DR

This paper introduces a UAV-based computer vision framework combining deep learning and machine learning to accurately count mounds in forestry sites, reducing manual effort and errors in large-scale planting assessments.

Contribution

The novel framework integrates instance segmentation and patch-level correction to improve mound counting accuracy from UAV imagery, addressing limitations of visual recognition alone.

Findings

Outperformed manual counting in precision

Effective in challenging conditions with erosion and occlusion

Demonstrated potential for large-scale forestry management

Abstract

Site preparation by mounding is a commonly used silvicultural treatment that improves tree growth conditions by mechanically creating planting microsites called mounds. Following site preparation, the next critical step is to count the number of mounds, which provides forest managers with a precise estimate of the number of seedlings required for a given plantation block. Counting the number of mounds is generally conducted through manual field surveys by forestry workers, which is costly and prone to errors, especially for large areas. To address this issue, we present a novel framework exploiting advances in Unmanned Aerial Vehicle (UAV) imaging and computer vision to accurately estimate the number of mounds on a planting block. The proposed framework comprises two main components. First, we exploit a visual recognition method based on a deep learning algorithm for multiple object detection by pixel-based segmentation. This enables a preliminary count of visible mounds, as well as other frequently seen objects (e.g. trees, debris, accumulation of water), to be used to characterize the planting block. Second, since visual recognition could limited by several perturbation factors (e.g. mound erosion, occlusion), we employ a machine learning estimation function that predicts the final number of mounds based on the local block properties extracted in the first stage. We evaluate the proposed framework on a new UAV dataset representing numerous planting blocks with varying features. The proposed method outperformed manual counting methods in terms of relative counting precision, indicating that it has the potential to be advantageous and efficient in difficult situations.