Did you Know?
  • The Lappet-faced Vulture, a regular winter visitor to the DDCR, has a wingspan of 2.5-3 metres (8-10 feet)

Large Scale Explorations of Real-Time Image Mining Captured by Unmanned Aerial Vehicles (UAVs): Application to Environment Protection

Project Summary

The need to protect the environment has taken center stage in recent years as the damage to the Earth’s natural ecological balance has become increasingly tangible. As humans, we consume a large amount of plastic as it has become a cornerstone of many manufacturing and packaging processes. In fact, it is common practice to dispose of unwanted waste improperly in the form of litter and illegally dumped garbage. Whether this occurs on the streets, where many people simply drop their unwanted packaging, or during time spent in extra-urban environments, it is clear that littering has become a significant problem in many societies and contexts.

Deep learning methods have proven to be efficient in the image processing field. In fact, their introduction in this area has reshaped the way images are processed and understood. At the same time, this raised expectations with regards to performing and automating the processing and understanding of images, along with understanding their content. A large number of works have been performed and many methods have been proposed to tackle different aspects of the problem. The novelty in this project resides in two folds: it is the first time that the performances of existing algorithms will be evaluated in such large magnitude and diversity of experiments, which will support the investigators to propose new algorithms. The second novelty resides in the application of approach in the area of liter identification in the desert. In fact, the investigators are not aware of any existing efforts that suggest the application of image mining in the desert.

The investigators intend to work closely with the DDCR to help detect and monitor litter in three key areas; along the boundary fence, camp areas and driving routes throughout the reserve. This will directly aid the DDCR in coordinating litter recovery as well issuing fines to parties who have violated the regulations regarding litter. In addition to a potential monetary gain and reduction in resources dispensed in patrolling the reserve, the wildlife will benefit from a reduced exposure to harmful objects which they may mistake for food.

Research Objectives

  • Collect a significant quantity of data to test a number of existing methods in real-life situations.
  • Determine the constraints that affect the quality of image understanding in real situations.
  • Determine benchmarks to be used in the evaluation of future methods.
  • Create a set of methods and workflows to implement the findings of this project.

Methodology

This research will make use of modern image mining techniques to contribute to the area of environmental protection through automatic litter recognition and identification in wild areas. In this project, we will begin by creating a large scale and controlled experiments protocol to evaluate the top existing methods in research w.r.t. our context, i.e., litter identification in wild areas. We will then follow an experiment-based methodology in which real situations and real datasets (e.g., UAV captured images and videos) will be used for observation and learning models. These datasets will serve as a foundation for enabling the extraction of actionable knowledge. Once the experiments are performed; we will proceed to researching a method (or methods) that would integrate the knowledge and the learnings from the experimental phase.

At a high level, we will follow the steps shown in the next section.

  • Data collection and preparation: As a backbone of our research and experiments, the data to analyze will be related to litter in wild areas, i.e., desert in our case. As UAVs will be largely used here, the configuration of the experiments must be taken care of to ensure an objective assessment and interpretable quality of the outcomes. The datasets have to be tagged and classified to make sure that we can build the expected models.
  • Benchmarking of existing methods: Relying on the collected data, a large experimental setup will be put in place to evaluate the most recent and advanced techniques available in the literature to identify litter.
  • Model(s) development: The objective here is to elaborate new methods for supporting litter identification in wild areas. While developing the models, we should take into consideration that we are dealing with real-time and heterogeneous data taken under different constraints.
  • Implementation and Evaluation: This step will basically run across the entire project. Its objective will be to define the quality of the proposed methods and approaches in terms of effectiveness and performance. We will perform this objective through formal methods and subjective evaluations.

Expected Outcomes

  • The project will provide a set of data, described in detail and capturing different situations and constraints.
  • This report will contain a rigorous description of a set of experimental protocols that will govern the test of different methods and techniques in the literature.
  • This deliverable is the final outcome and expectation of the project. It is a software platform that is dedicated for end-users and which will allow them to analyze real-time streams coming from unmanned aerial vehicles. It will embed artificial intelligence techniques that will not only reduce, e.g., the noise in image streams but also the selection of the best configuration to use by the UAV depending on the external constraints. The software is expected at the end of the project in its final version.
  • We will target at least 2 top conferences/journals publications. These publications will be expected during the second year of the project, mainly.
  • This report is a classical final report. It will be provided at the end of the project and will describe the achieved objectives and outcomes of the project.