Leveraging ResNet Architectures for Enhanced Detection of Great Apes in Video Data

Mohanty, Rohit Rajat (2024) Leveraging ResNet Architectures for Enhanced Detection of Great Apes in Video Data. Masters thesis, Dublin, National College of Ireland.

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Abstract

Detecting great apes in their natural habitats is essential for conservation, but not solely for providing critical insights into their behavior and population dynamics. It is also to reduce the manual work done by researchers when trying to process thousands of videos, resulting is great use of labour and possible avenues for human error. This thesis investigates the performance and efficiency of a ResNet-101 model with integrated Spatial Convolutional Modules (SCM) and Temporal Convolutional Modules (TCM) for great ape detection and behavior recognition using the PanAf500 dataset. The study compares the implementation of this model on both GPU and TPU, evaluating metrics such as precision, recall, F1 score, mean Average Precision (mAP), training time, and resource usage. Contrary to common expectations, the initial findings indicate that the TPU implementation exhibited longer training times and higher validation loss compared to the GPU implementation, which benefited from a Cosine Annealing Learning Rate Scheduler. This discrepancy highlights the importance of workload optimization and batch size considerations for each platform. Additionally, the research encountered unusually high reported memory usage and model size metrics, suggesting potential measurement or reporting errors that require further validation. This research also explores the impact of model quantization on reducing computational resource requirements and improving generalization. This study contributes to the fields of machine learning and wildlife conservation, aligning with several UN Sustainable Development Goals (SDGs), including climate action, life on land, quality education, and industry innovation and infrastructure. It supports the development of optimized deep learning models for real-world conservation efforts, enhancing the ability to monitor and protect great apes in their natural environments and more importantly, reduce the labour of researchers in data processing for large volumes of video data.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Milosavljevic, Vladimir UNSPECIFIED
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science S Agriculture > SF Animal culture Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	20 Aug 2025 10:34
Last Modified:	20 Aug 2025 10:34
URI:	https://norma.ncirl.ie/id/eprint/8589

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