An analysis of altitude, citizen science and a convolutional neural network feedback loop on object detection in Unmanned Aerial Systems

Bowley, Connor; Mattingly, Marshall; Barnas, Andrew F.; Ellis‐Felege, Susan N.; Desell, Travis

doi:10.1016/j.jocs.2019.04.010

Cited by 15 publications

(13 citation statements)

References 18 publications

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“…Both of these platforms incorporate citizen science to varying extents. An instance of the effective integration of citizen scientist in deep learning is Wildlife@Home, where citizen science classifications were used to train neural networks that help to analyse bird populations [84]. The growing set of citizen science classified datasets, in addition to several research teams' datasets [85,86], are also being used to create software packages to aid ecological projects.…”

Section: Integrating Ai Into Camera Trap and Citizen Science Work Flowsmentioning

confidence: 99%

Innovations in Camera Trapping Technology and Approaches: The Integration of Citizen Science and Artificial Intelligence

Green

Rees

Stephens

et al. 2020

Animals

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Simple Summary: Camera traps, also known as "game cameras" or "trail cameras", have increasingly been used in wildlife research over the last 20 years. Although early units were bulky and the set-up was complicated, modern camera traps are compact, integrated units able to collect vast digital datasets. Some of the challenges now facing researchers include the time required to view, classify, and sort all of the footage collected, as well as the logistics of establishing and maintaining camera trap sampling arrays across wide geographic areas. One solution to this problem is to enlist or recruit the public for help as 'citizen scientists' collecting and processing data. Artificial Intelligence (AI) is also being used to identify animals in digital photos and video; however, this process is relatively new, and machine-based classifications are not yet fully reliable. By combining citizen science with AI, it should be possible to improve efficiency and increase classification accuracy, while simultaneously maintaining and promoting the benefits associated with public engagement with, and awareness of, wildlife.Abstract: Camera trapping has become an increasingly reliable and mainstream tool for surveying a diversity of wildlife species. Concurrent with this has been an increasing effort to involve the wider public in the research process, in an approach known as 'citizen science'. To date, millions of people have contributed to research across a wide variety of disciplines as a result. Although their value for public engagement was recognised early on, camera traps were initially ill-suited for citizen science. As camera trap technology has evolved, cameras have become more user-friendly and the enormous quantities of data they now collect has led researchers to seek assistance in classifying footage. This has now made camera trap research a prime candidate for citizen science, as reflected by the large number of camera trap projects now integrating public participation. Researchers are also turning to Artificial Intelligence (AI) to assist with classification of footage. Although this rapidly-advancing field is already proving a useful tool, accuracy is variable and AI does not provide the social and engagement benefits associated with citizen science approaches. We propose, as a solution, more efforts to combine citizen science with AI to improve classification accuracy and efficiency while maintaining public involvement.

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Section: Integrating Ai Into Camera Trap and Citizen Science Work Flowsmentioning

confidence: 99%

Innovations in Camera Trapping Technology and Approaches: The Integration of Citizen Science and Artificial Intelligence

Green

Rees

Stephens

et al. 2020

Animals

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“…They proposed a loss function called Dice loss that assigns equal importance to the false negatives and the false positives. In computer vision, Bowley et al (2019) developed an automated feedback loop method to identify and classify wildlife species from Unmanned Aerial Systems imagery, for training CNNs to overcome the unbalanced class issue. On their expert imagery dataset, the error rate decreased substantially from 0.88 to 0.05.…”

Section: Related Workmentioning

confidence: 99%

“…Feedback Loop To address class imbalances in text classification, this work adapts the approach in Bowley et al (2019) from the computer vision domain. The goal of this approach is not only to alleviate the bias towards majority classes but also to adjust the training data instances such that the models are always being trained on the instances that was performing the worst on.…”

Section: Handling Class Imbalancementioning

confidence: 99%

“…This extreme class imbalance is an obstacle when processing logbooks as it causes most learning algorithms to become biased and mainly predict the large classes (Kim et al, 2019). To overcome this issue, we introduce a feedback loop strategy, which is a repurposing of a method used to address extreme class imbalance in computer vision (Bowley et al, 2019)…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Handling Extreme Class Imbalance in Technical Logbook Datasets

Akhbardeh¹,

Alm²,

Zampieri³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Technical logbooks are a challenging and under-explored text type in automated event identification. These texts are typically short and written in non-standard yet technical language, posing challenges to off-the-shelf NLP pipelines. The granularity of issue types described in these datasets additionally leads to class imbalance, making it challenging for models to accurately predict which issue each logbook entry describes. In this paper we focus on the problem of technical issue classification by considering logbook datasets from the automotive, aviation, and facilities maintenance domains. We adapt a feedback strategy from computer vision for handling extreme class imbalance, which resamples the training data based on its error in the prediction process. Our experiments show that with statistical significance this feedback strategy provides the best results for four different neural network models trained across a suite of seven different technical logbook datasets from distinct technical domains. The feedback strategy is also generic and could be applied to any learning problem with substantial class imbalances.

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“…Applications of CNNs to drone imagery have been growing during the past decade 31 . For instance, in koalas (Phascolarctus cinereus) 32 , cetaceans 33 , olive ridley sea turtles (Lepidochelys olivacea) 34 , kiang (Equus kiang) 35 , birds [36][37][38][39][40] and a set of African terrestrial mammals [41][42][43] . Depending on the quality of the imagery and the amount of training data, evidence shows that precision and accuracy of detections using CNNs can be high, in some cases better than human counts 25 .…”

mentioning

confidence: 99%

Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys

Lenzi

Barnas

ElSaid

et al. 2023

Sci Rep

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Imagery from drones is becoming common in wildlife research and management, but processing data efficiently remains a challenge. We developed a methodology for training a convolutional neural network model on large-scale mosaic imagery to detect and count caribou (Rangifer tarandus), compare model performance with an experienced observer and a group of naïve observers, and discuss the use of aerial imagery and automated methods for large mammal surveys. Combining images taken at 75 m and 120 m above ground level, a faster region-based convolutional neural network (Faster-RCNN) model was trained in using annotated imagery with the labels: “adult caribou”, “calf caribou”, and “ghost caribou” (animals moving between images, producing blurring individuals during the photogrammetry processing). Accuracy, precision, and recall of the model were 80%, 90%, and 88%, respectively. Detections between the model and experienced observer were highly correlated (Pearson: 0.96–0.99, P value < 0.05). The model was generally more effective in detecting adults, calves, and ghosts than naïve observers at both altitudes. We also discuss the need to improve consistency of observers’ annotations if manual review will be used to train models accurately. Generalization of automated methods for large mammal detections will be necessary for large-scale studies with diverse platforms, airspace restrictions, and sensor capabilities.

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An analysis of altitude, citizen science and a convolutional neural network feedback loop on object detection in Unmanned Aerial Systems

Cited by 15 publications

References 18 publications

Innovations in Camera Trapping Technology and Approaches: The Integration of Citizen Science and Artificial Intelligence

Innovations in Camera Trapping Technology and Approaches: The Integration of Citizen Science and Artificial Intelligence

Handling Extreme Class Imbalance in Technical Logbook Datasets

Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys

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