Enhancing Forestry Object Detection using Multiple Features

Sammanfattning: In this Master's project increasing the performance of object detection in forestry environment, based on the extracted features is studied. There are several object detection projects for robots which are based on feature calculation and extraction. An example of these kinds of projects is the sugarbeet project [3] that has inspired the feature selection and calculations parts presented in this report. Extracted feature sets are given to several classifiers and their results are merged and fused such that the overall performance of the forestry object detection increases. Furthermore different supervised and unsupervised methods of dimensionality reduction are applied on the feature set as an approach to improve classiffcation accuracy. Comparison between the output classification performance of dimensionality reduction methods show that applying supervised methods result in improving the classification performance by about 12 percent.