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Investigating automated bird detection from webcams using machine learning
One of the most challenging problems faced by ecologists and other biological researchers today is to analyze the massive amounts of data being collected by advanced monitoring systems such as camera traps, wireless sensor networks, high-frequency radio trackers, global positioning systems, and sate...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2022
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| _version_ | 1867614233947013120 |
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| access_status_str | Open Access |
| author | Mirugwe, Alex |
| author2 | Nyirenda, Juwa |
| author_browse | Mirugwe, Alex Nyirenda, Juwa |
| author_facet | Nyirenda, Juwa Mirugwe, Alex |
| author_sort | Mirugwe, Alex |
| collection | Thesis |
| description | One of the most challenging problems faced by ecologists and other biological researchers today is to analyze the massive amounts of data being collected by advanced monitoring systems such as camera traps, wireless sensor networks, high-frequency radio trackers, global positioning systems, and satellite tracking systems being used today. It has become expensive, laborious, and time-consuming to analyze the large datasets using manual and traditional statistical techniques. Recent developments in the field of deep learning are showing promising results towards automating the analysis of these extremely large datasets. The primary objective of this study is to test the capabilities of the state-of-the-art deep learning architectures to detect birds in the webcam captured images. A total of 10592 images were collected for this study from the Cornell Lab of Ornithology live stream feeds situated in six unique locations in United States, Ecuador, New Zealand, and Panama. To achieve the main objective of the study, two convolutional neural network object detection meta-architectures, single-shot detector (SSD) and Faster R-CNN in combination with MobileNet-V2, ResNet50, ResNet101, ResNet152, and Inception ResNet-V2 feature extractors were studied and evaluated. Through the use of transfer learning, all the models were initialized using weights pre-trained on the MS COCO (Microsoft Common Objects in Context) dataset provided by the TensorFlow 2 object detection API. The Faster R-CNN model coupled with ResNet152 outperformed all other models with a mean average precision of 92.3%. However, the SSD model with the MobileNet-V2 feature extraction network achieved the lowest inference time (110ms) and the smallest memory capacity (30.5MB) compared to its counterparts. The outstanding results achieved in this study confirm that deep learning-based algorithms are capable of detecting birds of different sizes in different environments and the best model could potentially help ecologists in monitoring and identifying birds from other species present in the environment. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/36492 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:48:47.870Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/36492 Investigating automated bird detection from webcams using machine learning Mirugwe, Alex Nyirenda, Juwa Dufourq, Emmanuel Statistical Sciences One of the most challenging problems faced by ecologists and other biological researchers today is to analyze the massive amounts of data being collected by advanced monitoring systems such as camera traps, wireless sensor networks, high-frequency radio trackers, global positioning systems, and satellite tracking systems being used today. It has become expensive, laborious, and time-consuming to analyze the large datasets using manual and traditional statistical techniques. Recent developments in the field of deep learning are showing promising results towards automating the analysis of these extremely large datasets. The primary objective of this study is to test the capabilities of the state-of-the-art deep learning architectures to detect birds in the webcam captured images. A total of 10592 images were collected for this study from the Cornell Lab of Ornithology live stream feeds situated in six unique locations in United States, Ecuador, New Zealand, and Panama. To achieve the main objective of the study, two convolutional neural network object detection meta-architectures, single-shot detector (SSD) and Faster R-CNN in combination with MobileNet-V2, ResNet50, ResNet101, ResNet152, and Inception ResNet-V2 feature extractors were studied and evaluated. Through the use of transfer learning, all the models were initialized using weights pre-trained on the MS COCO (Microsoft Common Objects in Context) dataset provided by the TensorFlow 2 object detection API. The Faster R-CNN model coupled with ResNet152 outperformed all other models with a mean average precision of 92.3%. However, the SSD model with the MobileNet-V2 feature extraction network achieved the lowest inference time (110ms) and the smallest memory capacity (30.5MB) compared to its counterparts. The outstanding results achieved in this study confirm that deep learning-based algorithms are capable of detecting birds of different sizes in different environments and the best model could potentially help ecologists in monitoring and identifying birds from other species present in the environment. 2022-06-22T08:30:14Z 2022-06-22T08:30:14Z 2022 2022-06-22T08:29:24Z Master Thesis Masters MSc http://hdl.handle.net/11427/36492 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Statistical Sciences Mirugwe, Alex Investigating automated bird detection from webcams using machine learning |
| thesis_degree_str | Master's |
| title | Investigating automated bird detection from webcams using machine learning |
| title_full | Investigating automated bird detection from webcams using machine learning |
| title_fullStr | Investigating automated bird detection from webcams using machine learning |
| title_full_unstemmed | Investigating automated bird detection from webcams using machine learning |
| title_short | Investigating automated bird detection from webcams using machine learning |
| title_sort | investigating automated bird detection from webcams using machine learning |
| topic | Statistical Sciences |
| url | http://hdl.handle.net/11427/36492 |
| work_keys_str_mv | AT mirugwealex investigatingautomatedbirddetectionfromwebcamsusingmachinelearning |