Vegetation classification using DeepLabv3+ and YOLOv5Download PDF

Published: 03 Jun 2022, Last Modified: 23 May 2023IFRRIA PosterReaders: Everyone
Keywords: Computer vision, classification, deep learning, forestry robotics
Abstract: Semantic segmentation and object detection are challenging tasks in computer vision. In recent years the performance of semantic segmentation and object detection has been greatly improved by using deep learning techniques. A large number of novel methods have been proposed to achieve the best results ranging from autonomous vehicles, humancomputer interaction, robotics, medical research, agriculture and virtual and augmented reality systems. In this work it is presented two methodologies to classify vegetation in complex environments, namely forests, using Deep Learning techniques. Deep Learning methods were used to classify vegetation for forest fires fuel/dry vegetation cleansing and also autonomous navigation. A key challenge for autonomous navigation in cluttered outdoor environments is the reliable discrimination between obstacles that must be avoided at all costs, and obstacles/objects that need to be identified to pursue the intended action of the robot. In this paper it is presented a brief study of the state of the art in object detection and also semantic segmentation. Also it is presented results of DeepLabv3+ semantic segmentation and YOLOv5 object detection of vegetation for an Unmanned Ground Vehicle (UGV) to clean forest fires fuel in forest complex environments.
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